rafmacalaba/datause-encoder-data huggingface dataset:
Table 3 reports the cross-sectional regression results of predicting income loss and work stoppage by real-time indicators using the first wave of data collected between April and August 2020.<sup>12</sup> Column 1 shows the results for the share of households reporting total income declines. Our preferred measure of overall model fit is the mean R<sup>2</sup> over the full set of one hundred imputations, which is 0.37. More frequent Google searches for “money” and “jobs”, and less frequent searches for “free food” are statistically significant correlates of the prevalence of total income declines. Columns 2-4 show the results for different types of income. The predictive power for a loss of farm income, nonfarm income, and wage income is significantly less, at 0.09, 0.13, and 0.24, respectively. The predictive power of real-time indicators for job loss is slightly lower than total income but substantially higher than those of the different types of income (average R<sup>2</sup> = 0.34).

The signs of the coefficients when analyzing the initial wave are not always intuitive. For example, searches for “bread” are negatively correlated with total income declines, searches for “Food Bank” are negatively correlated with the prevalence of wage income declines, and searches for “Free Food” are negatively correlated with the prevalence of work stoppage. This likely reflects correlations between Google search behavior and initial conditions of the regions, such as their overall level of development. For example, residents in poorer areas may be more likely to search for food banks or free food in response to an adverse shock, even if those shocks were more prevalent in more affluent areas. Similarly, searches for “jobs” and “unemployment” are negatively correlated with the prevalence of total income declines in the first wave, suggesting that areas hit less hard by the crisis, in terms of the prevalence of income declines, may have been more likely to search for unemployment insurance or new jobs. More generally, the strong correlations in the analysis of the first wave partly reflect the role of precrisis regional characteristics that are correlated with both the real-time indicators and the intensity of the shock.

The correlations with mobility data are more intuitive, as workplace mobility was negatively correlated with the prevalence of wage income declines. This reflects the fact that the mobility data is measured as a change from a pre-crisis baseline. Google searches for “job postings” are positively correlated with wage income declines, as one would expect. Google searches for

> 12 Each survey wave is associated with a month, and the real-time indicators for that month were used.

18
23

# **Figure 4a SME Loans/Total Loans (%): GCC vs. Non-GCC Bank**

<!-- Start of picture text --> 0 2 4 6 8 10 0 20 40 60 80<br>SME Loans % of Total Loans - GCC SME Loans % of Total Loans - Non-GCC<br>Skewness: 1.39 Skewness: 1.52<br>Kurtosis: 3.94 Kurtosis: 5.78<br>Figure 4b<br>SME Loans/Total Loans (%): State vs. Private Bank<br>0 10 20 30 40 0 20 40 60 80<br>SME Loans % of Total Loans - State SME Loans % of Total Loans - Private<br>Skewness: 1.35 Skewness: 1.96<br>Kurtosis: 3.90 Kurtosis: 7.36<br>10 10<br>8 8<br>6 6<br>Frequency<br>4 4<br>2 2<br>0 0<br>3 30<br>2 20<br>Frequency<br>1 10<br>0 0<br><!-- End of picture text -->
<!-- Start of picture text --> oss 0.2<br>on Noo oa<br>ou “Teese 0.08<br>o oe a<br>005 SP EFE EE SS ES S SEPPS SFLP SSS SF<br>XQ “a ens<br>7 ~ 002<br>23 -o@<br>-0.35 -0.08<br>A DP{VB PPPD DM> HPPM PP7 GHFMSV GPSF of HPOAGySMPMH SHM Oh PPHP WOPHwd AhMH GPA> ahMM OY?A oF© —Mexico's: G<br>+--+» USA's G<br>_— — — ——Rate ofIncrease of Share of Mexican Exports into US Imports<br><!-- End of picture text -->
Puerto Montt <!-- Start of picture text --> 6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>May-48 Oct-53 Abr-59 Oct-64 Mar-70 Sep-75 Mar-81 Ago-86 Feb-92 Ago-97 Feb-03 Jul-08<br>Temperature anomaly (oC)<br><!-- End of picture text --> Punta Arenas <!-- Start of picture text --> 4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>May-48 Oct-53 Abr-59 Oct-64 Mar-70 Sep-75 Mar-81 Ago-86 Feb-92 Ago-97 Feb-03 Jul-08<br>Temperature anomaly (oC)<br><!-- End of picture text --> _Source:_ Authors’ estimation based on data from the NCDC’s Monthly Climatic Data for the World.

Once we have the series of temperature anomalies, it is straightforward to test whether there is a significant trend. This is done simply by regressing the anomaly on a trendvariable, which has been scaled so that the coefficient can be directly interpreted as temperature change per decade in degrees Celsius. We use a confidence level of 95 percent to decide whether the trend is statistically significant, which means that the P-value should be less than 0.05 for the trend to be significant.

Table 7 shows the estimated trends for each of the seven stations in Chile. Of these, two stations show significant warming since the middle of the previous century; two show no significant change; and three show significant cooling.

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successful jobs search. If job creation is high, then it takes less time and it is easier to find a new job.

**Figure 2 Job creation and job destruction: Moldova against selected countries**

<!-- Start of picture text --> Job Reallocation: Continuing Firms<br>12.0 25.0<br>10.0<br>20.0<br>8.0<br>15.0<br>6.0<br>10.0<br>4.0<br>5.0<br>2.0<br>0.0 0.0<br>Croatia Poland France Moldova Bulgaria Lithuania<br>Job creation Job destruction Job reallocation<br><!-- End of picture text --> Source: Annual Survey of Enterprises, 2000 and 2001; author’s calculations.

Against this benchmark, the Moldavian labor market does not perform very well. Enterprise restructuring and associated reallocation of resources from less toward more productive uses is limited due to the low job creation rate. The job creation rate in _continuing_ enterprises in Moldova is only about two-thirds of that in Lithuania (Table 3). At the same time, the job destruction rate in Moldova is somewhat higher than in Lithuania. If few new jobs are being created there is not much scope for the reallocation of old jobs. Thus the low job creation puts a ceiling on job reallocation. As a result, the job reallocation rate is lower and enterprise restructuring is slower in Moldova than in Lithuania. To the extent job reallocation brings about productivity gains, slower pace of enterprise restructuring in Moldova translates into lower than otherwise productivity growth, and accordingly, lower income growth.

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with the more quantitative, resource-based, measures of the judiciary in the expected way, though none of the correlations is significant. States that dedicate a larger proportion of their budget to the judiciary have a higher level of judicial effectiveness (ρ=.25), as do states in which judges earn higher salaries (ρ=.20).<sup>15</sup> States in which trials take a longer time to prosecute rate lower on the judicial effectiveness scale (ρ= -.17).

A separate survey of business owners provides state level measures of the cost of regulation and a measure of generalized trust. The “Survey of Governance and Development of Enterprises in Mexico” (EGDE) was conducted by the Monterrey Institute of Technology (ITESM) in the first quarter of 2002. The survey gathered data from 3,969 firms, with at least 100 firms responding in each of Mexico’s 32 states. We use the responses to two questions from this survey. The first asks managers to identify several categories of costs as a percentage of the firm’s revenue. Among the categories is the cost of regulation and regulatory activities. Cross-country data on the cost of regulations related to the establishment of a business reported in Djankov et al. (2002) indicate that costs of regulation in Mexico are high but not exorbitant compared with other countries. Direct fees and time costs for establishing a business amount to 83 percent of GDP in Mexico, above the world average of 66 percent, but far from the highest in the world.

The EGDE survey indicates considerable variation in regulation costs across states within Mexico. The state level average cost of regulation is shown in the second

> than more efficiency in the legal system. The quantitative data produce results which are similar to those we find using the impressionistic data.

> 15 The ITAM/GMA survey report refers to general complaints in Mexico that judges are underpaid relative to what they might earn in the private sector. Our calculation of “real” judicial salaries divides nominal salary by the average earnings of full time workers with university degrees residing in urban areas in the same state. We find that in only 2 of Mexico’s 32 states do judges on courts of first instance receive salaries below those in the private sector.

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# **Table 2:  Disbursements from Official Creditors as Percentage of Total Government Spending**

|**Country**|**Disb/Gov**|**Country**|**Disb/Gov**|**Country**|**Disb/Gov**|
|---|---|---|---|---|---|
|GAMBIA,THE*|37.6%|BELIZE|15.6%|PERU|9.8%|
|MAURITANIA*|29.4%|SWAZILAND|15.4%|DOMINICA|9.5%|
|GUINEA-BISSAU*|28.1%|KENYA*|15.3%|CONGO,REP.*|9.2%|
|MADAGASCAR*|26.7%|HAITI*|15.0%|MAURITIUS|9.0%|
|MALI*|24.0%|COSTA RICA|14.7%|MALDIVES*|8.9%|
|GHANA*|23.6%|CAPE VERDE|14.1%|COLOMBIA|8.8%|
|GUYANA*|22.9%|EL SALVADOR|14.1%|ST. LUCIA|8.7%|
|MOZAMBIQUE*|22.8%|JAMAICA|13.9%|EGYPT|7.9%|
|LAO P.D.R.*|22.5%|JORDAN|13.8%|ALBANIA|7.2%|
|GUINEA*|22.4%|PARAGUAY|13.7%|MOLDOVA*|7.1%|
|BURKINA FASO*|22.3%|TOGO*|13.7%|PAPUA NEW GUINEA|7.1%|
|MALAWI*|21.7%|TUNISIA|13.7%|MACEDONIA|6.8%|
|HONDURAS*|21.6%|PHILIPPINES|13.5%|YEMEN*|6.7%|
|NICARAGUA*|21.6%|LESOTHO*|13.4%|PANAMA|6.2%|
|NEPAL*|20.5%|ST. VINCENT & GREN.|12.9%|THAILAND|6.1%|
|NIGER*|20.1%|SRI LANKA*|12.8%|BOTSWANA|5.8%|
|CHAD*|19.8%|LIBERIA*|12.8%|SUDAN*|5.8%|
|SENEGAL*|19.7%|CAMEROON*|12.7%|VANUATU|5.4%|
|TAJIKISTAN*|19.3%|INDONESIA|12.0%|FIJI|5.3%|
|TANZANIA*|19.0%|MOROCCO|12.0%|UZBEKISTAN*|5.2%|
|UGANDA*|18.9%|COMOROS*|11.9%|ALGERIA|5.1%|
|KYRGYZ REPUBLIC*|18.8%|MONGOLIA*|11.8%|SEYCHELLES|5.1%|
|SIERRA LEONE*|18.8%|GRENADA|11.3%|URUGUAY|5.0%|
|BURUNDI*|18.7%|GEORGIA*|11.3%|GABON|4.7%|
|BANGLADESH*|18.6%|GUATEMALA|11.2%|ROMANIA|4.3%|
|PAKISTAN*|18.4%|SAO TOME & PRINCIPE|11.2%|BULGARIA|4.1%|
|BENIN*|17.6%|ST. KITTS & NEVIS|11.2%|SYRIA|3.9%|
|ARMENIA*|17.5%|DJIBOUTI*|11.1%|TURKEY|3.5%|
|ETHIOPIA*|17.4%|ECUADOR|11.1%|SOLOMON ISLANDS*|3.4%|
|ZAMBIA*|17.2%|VIETNAM*|10.9%|ANGOLA*|3.3%|
|BOLIVIA*|17.1%|ERITREA*|10.7%|MALAYSIA|3.3%|
|BHUTAN*|16.3%|COTE D`IVOIRE*|10.4%|LATVIA|3.1%|
|RWANDA*|16.1%|CENTRAL AFR. REP.*|10.3%|||
|DOMINICAN REPUBLIC|15.8%|CONGO,DEM. REP.*|10.2%|||
|CAMBODIA*|15.6%|TONGA|10.0%|||
|||||**_Averages_**||
|||||Full Sample|13.4%|
|||||IDA Sample|16.2%|
|||||High Disb Sample|16.7%|

Note: This table lists the 102 countries that make up the full sample, together with the over-time average of disbursements on loans from official creditors as a fraction of total government spending, for each country. These countries satisfy the criteria that (a) disbursements on loans from official creditors average at least one percent of GDP over the period 1970-2010, and (b) at least 15 annual observations on government spending are available. IDA-eligible countries that make up the IDA subsample are indicated with asterisks. The high disbursements subsample consists of countries in the first two columns, in which disbursements on loans from official creditors average more than 10 percent of government spending.

36
such as basic currency and interest rate derivatives. It focuses on currencies and maturities that are not covered by regular market providers. As of November 2008, TCX has approved 49 benchmarks in 60 countries. TCX’s current paid-in and committed Tier 1 and Tier 2 capital is equal to US$585 million.

5.18 **Leading the World Bank Group’s efforts in this area, IFC committed more than US$5.3 billion equivalent of local currency loans using derivatives in 26 different currencies, the bulk of which focused on the financial and productive sectors.** IFC works actively with swap market counterparties to increase the number of hedging currencies that are available. In 2007 and 2008, it committed loans using derivatives for the first time in Ghanaian cedi, Kenyan shillings, Nigerian naira, Romanian leu, Vietnamese dong, and Zambian kwacha. As Figure 5.1 illustrates, 63 percent of IFC local currency lending went to the financial sector, followed by 11 percent in industry and trade sectors. More than a third of these flows went to Latin America and the Caribbean, followed by East Asia and the Pacific, and Eastern Europe and Central Asia.

**<u>Figure 5.1: IFC Local Currency Lending By Sector and Region, FY1997–2009</u>**

<!-- Start of picture text --> 9 % 0 % 2 % 1 %2 % 3%<br>2 1 % 1 4%<br>1 6 %<br>4 %<br>1 7 % 11 %<br>6 3%<br>3 7 %<br>E a s t A s i a a n d P a c i fi c Agr icu ltu re, Fis h in g an d Fo res tryFi na nce<br>E u r o p e a n d C e n tr a l A s i a I nd us try a nd Trad e<br>La ti n A m e r i c a a n d th e C a r i b b e a n Hea lth a n d Other So cia l Ser vices<br>S o u t h A s i a I nfra s tru cture<br>I nfor ma ti o n a nd Co mm u ni catio n s<br>S u b ‐ S a h a r a n Af r i c a P ub li c Ad mi n is tra ti on , La w, an d Jus ti ce<br>O t h e r s Ener gy a n d Mi n in g<br>Source: IFC Treasury.<br><!-- End of picture text --> _Developing Private Catastrophe Insurance Markets_ 5.19 **Domestic catastrophe insurance markets (for example, for households, farmers, and herders) are under-developed in middle- and lower-income countries. Demand and supplyside constraints hinder the emergence of markets for property catastrophe insurance and agriculture insurance.** While more than 40 percent of the direct losses from natural disasters are insured in developed countries, less than 10 percent of these losses are covered by insurance in middle-income countries and less than 5 percent in low-income countries.<sup>54</sup> On the demand side, development of efficient markets is undermined by low nonlife insurance penetration, inadequate awareness of catastrophic risk exposure, and limited ability to pay insurance premiums due to low incomes. On the supply side, these countries have limited technical capacity, undercapitalization of local insurers, and regulatory impediments.

> 54 Cummins and Mahul, 2008.

32
Figure 9: Trends around introducing a new export variety <!-- Start of picture text --> −3 −2 −1 0 1 2<br>intronewprodANYVE<br>(a) Investment<br>−3 −2 −1 0 1 2 −3 −2 −1 0 1 2<br>intronewprodANYVE intronewprodANYVE<br>(b) Technology Transfers Investment (c) R&D Investment<br>9<br>8.5<br>logI_t<br>8<br>7.5<br>7.5 6.5<br>6<br>7<br>logRDreal 5.5<br>logtechtransfreal<br>6.5 5<br>4.5<br>6<br><!-- End of picture text --> Our observations are also consistent with findings based on Chilean firm-level data which show that firms build up their capital stocks before entering export markets (Alvarez and L ́opez 2005, Emami Namini and L ́opez 2006).

These observations are also compatible with anecdotal evidence obtained during an interview with a leading Mexican juice producer. The company executive confirmed that introducing a new product into export markets requires significant preparations and investment outlays. Consumers in the U.S. (which is the major export market for this producer) demand higher-quality/higher-price products than the average Mexican consumer. For instance, they prefer juices closer in taste to fresh juices to products from concentrates. The company recently acquired a new technology and introduced such juices targeting higher-end Mexican consumers and subsequently started selling them in export markets. Moreover, all export products must be in compliance with sanitary and phytosanitary requirements of the destination country which again may require changes to the production process. Furthermore, exports may also require different type of packaging. For instance, while Mexican consumer prefer cartons, US buyer have a preference for plastic and glass containers. In the juice industry, package attractiveness plays a very important role. This 26
**How to do it?** To finance additional public spending, the government should increase its revenues as a share of GDP by strengthening tax administration and adjusting excise taxes, continue lowering the debt-to-GDP ratio and interest payments, and restrain non-priority current expenditures. To increase tax collection, the taxpayer register should include the corporate conglomerates and tax arrears should be audited. Indexation would ensure that excise revenues do not decline in real terms: excises on fuel, alcohol, and tobacco are low by international standards and have not kept up with inflation since the tax reform of 1997 (IMF, 2006b). Finally, starting from the economic zones, it is necessary to stimulate risk-sharing among investors - for example, via PPP in infrastructure, by co-financing public works (transport and communications) and in education, by addressing under-provision of training in areas where skills are lacking.

# **6c. Gradually reduce** **_élite_ -capture, to lower the cost of strategic inputs**

**In the traditional sectors, the rent-seekers are powerful and well established.** In the Philippines, the status quo has historical roots: the colonial distribution of factor endowments determined the power and incentives of self-interested political constituencies, which, in equilibrium, profit from oligopolistic privileges and perpetuate them. Well-established in sectors that are strategic because of multiple backward and forward linkages, the conglomerates - by producing expensive inputs - skim rents from the economy and shrink the margins of the potentially most dynamic agents: the small and medium domestic private producers. They also control bank credit and dominate state procurement contracts through political connections.

**Less** **_élite_ -capture in agriculture, maritime and air transport, electricity, and cement.** To attract investment in the capital-intensive sectors, the corporate conglomerates should be restrained in their use of political connections, and their rents should be trimmed down by reduced economic protection. For example, promoting competition in the agriculture sector would lower prices, improve competitiveness in the food processing and livestock industries,

40
We have documented that in the developing world millions of people are purchasing energy intensive durables. In the previous section, we documented that even California features a large durable set of fossil fueled vehicles. In this section, we present additional evidence from Europe documenting that within each of the continent’s nations there are geographic clusters of higher carbon footprint places. High emissions regions have incentives to lobby their local officials to protect them.

Previous research on the United States has documented how local carbon emissions influence carbon politics. Eyer and Kahn (2020) document efforts by coal states such as West Virginia to use local policies to protect coal miner jobs. Cragg et al. (2013) document that Congressional voting on enacting Cap and Trade legislation is associated with the area’s local per-capita carbon footprint. Representatives are more likely to vote in favor of carbon pricing if their district’s emissions are lower.

Within nations, there can also be geographic areas that oppose carbon pricing because the local economy is carbon intensive. A region can have a large carbon footprint because of the industries concentrated there, the area’s low population density, and the types of power generation used to generate power. To explore this point, we use data from jurisdictions within European nations. The raw data are from https://openhgmap.net/data.<sup>3</sup> In Table 6, we report the average carbon footprint and the coefficient of variation across cities within the same nation in Europe. World carbon dioxide emissions per-capita in 2018 was 4.5 tons.<sup>4</sup> These results indicate that there are high emitting regions even in relatively low carbon nations such as France and Germany. Poland features a high coefficient of variation indicating that there are high carbon areas. The people and the political leaders of these regions will be less likely to support the low carbon agenda and they are likely to argue that Pigouvian policies are elitist unless there are spatial transfers to such regions. The United States has wrestled with how to compensate coal miners for their anticipated dislocation (Eyer and Kahn 2020).

> 3 This project maps CO2 emissions across Europe. The aim is to estimate an emissions inventory for each of the ~116 000 administrative jurisdictions across Europe and the UK. The model spatially disaggregates each country's official (Eurostat) CO2 emissions inventory to places using OpenStreetMap. Vehicle emissions are attributed across fuel stations, train emissions at stations, aviation bunker fuel emissions at airports, and so on. Industrial source emissions are located at the registered address where these emissions physically occur or are legally controlled. Data are for the year 2018.

> 4 https://data.worldbank.org/indicator/EN.ATM.CO2E.PC

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# Table 12:<sup>Brazil -Simulated Reductions in Income</sup> Gap by Household<sup>Type and Metrpolitan Area, 1984</sup>

(percent or<sup>minimumsalaries)</sup>

||Reav|e Differenc|es||Simuta|ted Income<sup>G</sup><br>|<sup>ap</sup><br>|
|---|---|---|---|---|---|---|---|
|Area and<br>Housebold Type|Eamings<br>Capacity<br>|Capcity<br>D<br>Utilization|ependen<br>Ratio|Income<br>Gap|Earnin<br>Capacity<br>|Capacity D<br>Utilization|epenqd<br>Ratio|
|Recife<br>|061|118|0.97|0.22|-.40|0.36|0.23<br>|
|FHH<br>|.<br>055|.<br>112|<br>1.70|0.57|0.18|0.63|0.45|
|FHH wlchildren|.|.||||||
|Sao Paulo<br>|064|121|0.87|0.06|-.57|0.26|0.07<br> <br>|
|FHH<br>|.<br>055|.<br>118|<br>2.12|0.56|0.19|0.64|0.40<br>**0**|
|FHH w/children|.|.||||||
|Porto Algr<br>|072|120|0.93|-0.01|-.40|-0.21<br>|-0.02<br>|
|FHH<br>|.<br>062|.<br>117|2.33|0.52|0.32|-0.07|0.21|
|FHH wlchildren|.|.||||||

Source: author's<sup>tabulations</sup> divided by the<sup>average for all households.</sup> Notes: Relative<sup>difference is the average value for a given type of household</sup> inimcm is<sup>equal to the average income of the household YF I YC).Anegative value indicates</sup> type (YF) subtracted the average income of household from the average<sup>income of all the other houses</sup> type is above<sup>that of other households.</sup> It is reported<sup>in minimum salaries.</sup> (Yc) divided<sup>by Yc or (Yc -</sup> group (Yc) on<sup>the simulation variable.</sup> Simulated income eaD<sup>is the estimated</sup> average income<sup>of household</sup> type (Y,) if this<sup>group had the mean of household</sup>