rafmacalaba/data-use-mentions-extended huggingface dataset:
19 Markets apparently do not distinguish between different oil price levels in pricing Ras Gas' credit risk. Given the intricate economics of LNG it might simply be the case that investors lack the information to make the link between different oil price levels and Ras Gas' prospects. Alternatively, the high volatility of oil prices in the past decade might have induced the belief that oil prices never stay long enough in any of the three bands to trigger the corresponding economic and financial consequences. In this case, the relative unimportance accorded to oil prices might reflect the market's collective view that the revenue impact is only temporary and that contracts are not renegotiated unless the oil price settles permanently way beyond one of the trigger points.

# 7. _Conclusion_

Much of current corporate finance theory draws upon the view that a firm is a nexus of contract. Nowhere is this view more evident than in project finance where construction, operating, output purchase and financial contracts form an interwoven web that determines the project's value and risk. In this paper we analyze how in the face of contractual incompleteness risks are transmitted and allocated between the different contracts and investors. Of particular importance is the relationship between off-take and financial contracts because the former effectively serves as security for the latter. Since such long-term supply contracts are necessarily incomplete and subject to opportunistic behavior, one would expect that factors affecting the offtake agreement impact a project's riskiness. Using daily data from the Ras Gas project, we investigate the link between off-take contract and financial structure by relating Ras Gas bond spreads to credit spreads of the major output buyer, the Korean electricity monopoly Kepco, emerging market returns and variables affecting the economic prospects of the parties. We find evidence for risk-shifting in the sense that non-contractible risks arising from the 25 year sale and purchase agreement determine the pricing and riskiness of the Ras Gas bonds. Ras Gas essentially passes on the output contract risk to its investors who, in consequence, closely identify the company with the prospects and riskiness of the ultimate buyer.
dummies; and (vi) study urban non-tradables or informal employment. Since our residuals are estimated _relative to_ similar cities in the world, we may worry that adding more countries to the analysis could change the results. However, our sample captures close to three-fourths of the world’s urban population, which limits such possibility. Results are also very similar if we use the raw shares.

To conclude, _ceteris paribus_ cities dramatically vary in their sectoral composition across the world. Theil decompositions suggest that half of the variation comes from differences across countries, implying an important role for aggregate structural change.

sector. While some cities are production cities because of their high MFG shares (e.g., Guangzhou and Ho Chi Minh), other production cities have high shares of FIRE (e.g., Bangalore and Paris).

> 12Indeed, only the LAC sample has good coverage over several decades in IPUMS.

> 13Additional analysis (not shown but available upon request) shows that this evolution was driven by declines in the relative urban employment shares of MFG, not FIRE.

> 14In the appendix, we discuss why results should not depend on the use of different urban definitions. Relatedly, while the urban classification is not stable across countries and years, we do not use the population density of the administrative units to select urban observations. Some rural areas of the world have particularly high density, for example in Bangladesh, Burundi, Haiti and Rwanda, which are some of the densest _and_ least urbanized countries in the world. Other areas of the world are undoubtedly urban despite their low density, in particular sprawling suburban cities (e.g., many million-plus U.S. cities).
# **I. Introduction**

Economic crises can harm workers through earnings losses, job displacement, and firm closures (Jacobson et al., 1993). Despite the temporary nature of most crises, their effects can be long-lasting due to several factors. Firms may engage in restructuring with lasting negative impacts on productivity and employment that they are unwilling to reverse due to sunk costs. If firms’ scarring occurs, worker scarring can occur even for workers who remain with the firm. Additionally, workers’ adjustment depends on their outside labor market options (including the prevalence of informality) and on assistance programs in case of job displacement. While a well-established literature documents the long-run effects on workers and firms of permanent shocks, such as trade liberalization or the China import competition shock (e.g., Goldberg & Pavcnik (2003); Autor et al. (2014); Utar (2018); Dix-Carneiro & Kovak (2019); Utar (2014); Dix-Carneiro et al. (2021)), little is know about the long-run effects of temporary shocks. Moreover evidence on the compensatory role of assistance programs is limited to the United States (e.g., Autor et al. (2013, 2014); Yagan (2019)).

In this paper we tackle a key unexplored question: what persistent effects on workers and firms are to be expected from temporary shocks in a developing country context marked by informality and limited assistance programs? To do so, we examine the dynamic medium to long-run impacts of temporary foreign shocks ensuing from the 2008–2009 Global Financial Crisis (GFC), on workers and firms and the cushioning effects of assistance programs. We use fifteen years of administrative employer-employee data from Brazil over the period 2004-2017 covering the universe of formal workers linked with longitudinal firm census and customs data. We also link for the first time the employer-employee data with the national registry of lowincome households, the worker registry of unemployment insurance and benefits disbursed, and beneficiary records of worker training
controlling for the remaining explanatory variables. In general, the correlation is insignificant, indicating likely no correlation or weak correlation between the IV and the unobservable.

Since the literature emphasizes the importance of having non-weak IV, and the existing testing framework focuses on a single endogenous variable, we conduct our two-stage-leastsquares estimation using all pre-Taiping periods and one single post-Taiping period at a time, so that in total we conduct five 2SLS estimations (i.e., for the years 1880, 1910, 1953, 1982, and 2000), one endogenous variable at a time. When pooling all periods, the results are similar except that we do not have guidance on how to interpret test statistics. The results are in Table 4.

The IV results reinforce our conclusions. The IV satisfies the relevancy condition. All the F statistics for the five post-Taiping years are above 20, suggesting a non-weak IV (Staiger and Stock 1997; Baum et al. 2007). Moreover, the magnitudes become more pronounced. The population growth of the treatment group in 1880 (compared to 1820) is 52 percent lower than in the control group. And the population growth of the treatment group in 2000 (compared to 1820) is 67 percent lower than that in the control group. The population growth gap between the Taiping and control prefectures becomes stable around 1953.

# **V.  Property Rights, Distance to Nanjing, Fiscal Capacity, and Population Evolution**

We now investigate the channels through which the Taiping Rebellion affects population growth: property rights, distance-based stationary banditry, and local state capacity. **Property Rights** We examine whether relative to the Early Taiping areas (i.e., featuring the lack of clearlydefined land ownership), the Late Taiping areas (i.e., featuring clearer land ownership) has less wasteland and faster post-war population recovery. The data on wasteland cover 332 counties on their share of wasteland in the Taiping provinces in 1915. We have data on 151 Early Taiping counties, 95 Late
:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Competencies represent a set of skills that were anticipated to be sensitive to education and relevant for non-formal employment. The skills tested included reading and following instructions to apply fertilizer; making correct change during hypothetical market transactions; sending text messages and using the calculator on a mobile phone, and calculating profits under hypothetical business scenarios. All competency components are standardized to have a mean of zero and a standard deviation of one in the control group. Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
of their 2018 labor forces. These were followed by the Kyrgyz Republic and Tajikistan which incurred negative net migration of 17 percent and 12 percent respectively of their 2018 labor forces. Apart from Turkmenistan, which incurred negative net migration equivalent to nearly 6 percent of its labor force, the other oil and gas producers experienced small positive net migration as a share of their labor forces, although the net flow was negligible for Uzbekistan. Not all migrants are members of the labor force, but it is a reasonable assumption that a large majority of them are. Hence, we can derive a rough estimate of the magnitude of the reduction in the labor force emanating from net migration from the data in table 2. These data suggest that Georgia may have incurred a reduction of around a third in its labor force since the beginning of the century and Armenia a reduction of close to a quarter, with the Kyrgyz Republic and Tajikistan suffering smaller but still substantial falls in their labor forces.

**Table 2 Net Migration 2000-18 as Percentage of 2018 Labor Force**

||Labour|Net Migration|Net migration|
|---|---|---|---|
||force|2000‐18|2000‐18|
||millions|millions|% of 2018|
||2018||Labour force|
|Armenia|1.4|‐0.37|‐26.9|
|Azerbaijan|5.1|0.09|1.8|
|Georgia|2.0|‐0.76|‐37.3|
|Kazakhstan|9.3|0.17|1.9|
|Kyrgyz Republic|2.6|‐0.45|‐16.9|
|Tajikistan|2.6|‐0.31|‐12.0|
|Turkmenistan|2.6|‐0.15|‐5.8|
|Uzbekistan|15.5|0.02|0
levels above those of the non-Native population and twice the years of education than those of the group with the lowest educational attainment. Considering all outcomes, the group with the best outcomes has an average standardized outcome almost 9 percent of a standard deviation higher than the general population, while the group with the worst outcomes has an average outcome that is lower than the general population by 60 percent of a standard deviation. These large differences in outcomes across native groups stress the importance of not considering the native population of Argentina as homogeneous.

While there may be many possible determinants of the differences across groups, we show that there is one historical characteristic of these that correlates with their groups outcomes: groups that practiced agriculture before the arrival of the Spanish tend to have better outcomes today than those that were hunter gatherers. This is consistent with what was found for Sub-Saharan Africa by Michalopoulos et al. (2018).

# **2 Size and Distribution of Native Groups**

The main data used in this study come from the 2010 National Census collected by the National Institute of Statistics (Instituto Nacional de Estad ́ıstica y Censos, 2010). In particular, we use data collected with the “long-form” version of the census based on a probabilistic sample of households. In addition to the questions on the short form (regarding gender, age, level of education, dwellings’ characteristics, employment status, etc.), the long-form questionnaire includes questions on fertility, contributions to retirement plans, health insurance and whether the respondent is Afro-descendant or belongs to or is descendant from an indigenous or Native group (Instituto Nacional de Estad ́ıstica y Censos, 2010). The 2010 National Census is the first decennial census in Argentina to ask about racial or ethnic identification at the individual level which allowed for a description of the native population (Instituto Nacional de Estad ́ıstica y Censos, 2015a,b,c,d,e,f).<sup>34</sup> Table 1 shows the population
# **1. Introduction**

The COVID-19 pandemic continues to test global food and social protection systems at an unprecedented scale. Initially starting as a global health crisis, the pandemic has evolved into a major economic crisis, causing disruptions to global economic systems, livelihoods and health. Several waves of the pandemic have prolonged the suffering of households by delaying economic recovery. Developing countries with poor public health infrastructure, limited social protection systems, high levels of food insecurity and poverty rates were projected to be disproportionally affected. The impacts of the pandemic were expected to be highest in African countries, which host about 70 percent of the world’s poor. Early projections were particularly pessimistic about the potential impact of the pandemic on poverty, both globally and for Africa (e.g., Laborde et al., 2020; Sumner et al., 2020; Mahler et al., 2020a; Mahler et al., 2020b). On the other hand, national accounts data on actual GDP growth for 2020 provide a more positive picture than these early projections (Pauw and Thurlow, 2022; McDermott and Swinnen, 2022).

The uncertainty in poverty projections is not surprising because the pandemic has evolved in an unpredictable way affecting various regions, economic sectors, and households differently. The pandemic had a large negative impact on African economies, but effects vary across countries and sectors (Zeufack et al., 2021). Several studies have shown that services and manufacturing sectors suffered the most while agriculture appeared to be relatively resilient (e.g., Arndt et al., 2020; Zeufack et al., 2021; McDermott and Swinnen, 2022). Informal sector workers, the poor, women, and youth experienced the greatest impact. In many cases, urban dwellers primarily employed in the industry and service sectors, including trade, transport and hotel services, were disproportionally affected by the economic fallout and associated lockdowns and mobility restrictions.<sup>1</sup> The impacts of the pandemic were
# **_5.  The Variable Selection and Description of Data_**

## **_i. Variable Selection_**

The analysis employs annual data covering the period from1959 until 2013. Data were obtained from the IMF’s International Financial Statistics (IFS) database, and were last accessed in January 2015. The data series that was obtained included the following:

- **Monetary Aggregates:** this is defined as nominal local currency in circulation in addition to demand deposits and time deposits (M2) deflated by the CPI with base year 2010 in logarithms.

- **Scale Variable:** This is a measure of activity of the real sector that reflects transaction demand for money. The volume of transactions is greater as the output grows. The scale variable will be represented as real GDP deflated by the CPI with a base year of 2010 in logarithms. The real consumption will also be employed to assess the feasibility of both variables and identify the most appropriate scale variable for modeling the demand for money in Egypt. This paper will assume absence of money illusion following Tobin (1972).<sup>15</sup> This means that people will not behave differently when they receive payoff information in real or nominal terms. Dropping this assumption is beyond the scope of this paper.

- **Price Variable:** The price variable is included to represent yields on real assets as alternatives to holding money. This will reflect on individuals' preferences of portfolio holdings. The choice of the price variable has been constrained by data availability and the degree of the openness of the economy (Sriram 1999). The literature has shown flexibility using a GDP deflator, a GNP deflator, and a wholesale price index in addition to the consumer price index. The Egyptian economy has been suffering from chronic inflation problems. Accordingly, inflation should be included as an opportunity cost for real assets in Egypt. This paper will use CPI to compute inflation as a price variable since it is the best available consistent
>|1597<br>|1603<br>|1586 Month<br>|0-3.99<br>||
|Nicaragua<br>|LSMS<br>|1993<br>|National<br>|4200<br>|514<br>|520<br>|511 Month<br>|0-4.99<br>||
|Pakistan<br>|LSMS<br>|1991<br>|National<br>|4800<br>|3773<br>|4051<br>|4127 Month<br>|0-4.99<br>||
|Peru<br>|LSMS<br>|1994<br>|National<br>|3623<br> <br>|2093<br>|2110<br>|2075 Month<br>|0-4.99<br>|i<sup>f</sup>|
|Philippines|Cebu Longitudinal Health and<br>Nutriton Survey|1991<br>|Regional<br>|<br>2264<br>|2033<br>|2036<br>|2139 Year<br>|0-4.99<br>|The survey area Is the city of Cebu, the regon<sup>center o</sup><br>Central Vlsayas region.|
|Romania|LSMS<br>|1996<br>|National<br>|36000<br>|3740<br>|3755<br>|3737 Month<br>|**0-4.99**<br>||
|Russia|Russia Longitudinal Monitoring<br>S|1997|National|3750|386|417|377 Month|0-4.99||
||urvey|||||||||
|South Afric|a LSMS<br>|1993<br>|National<br>|9000<
93

# **Appendix 3: Fuel Prices and Pass-Through Coefficients**

Figures A3.1–A3.4 show retail prices in the study countries in July 2012 for gasoline, diesel, kerosene, and LPG, respectively. FOB prices of the fuels in four major refining centers— Northern Europe, Arab Gulf, Singapore, and U.S. Gulf Coast—are noted for comparison. Because of transport, storage, and retailing costs, taxes and other fees, and profit margins at each stage in the supply chain, retail prices are considerably higher than the benchmark FOB prices in deregulated markets. If retail and FOB prices are close, that is likely to signal subsidies. Figure A3.5 plots the ratio of the price of kerosene to that of diesel in countries where information on kerosene prices was available.

Table A3.1 lists pass-through coefficients between January 2009 and July 2012. The calculation methodology and the sources of benchmark FOB prices are the same as those described in Kojima (2012), with one difference. A much more detailed study of the transmission of world oil price increases in 12 major EU countries found that the increases were passed on to consumers within three to five weeks (Meyler 2009), or about a month. These countries can safely be assumed to be passing on prices increases on the global oil market fully to their domestic markets. This study therefore takes reference benchmark prices from the month before―December 2008 and June 2012―as more appropriate measures of gauging the extent of pass through between two time periods. The sources of retail information are the same as those in Kojima (2012) except Uganda, where the price survey results reported by the Uganda Bureau of Statistics were used instead. As mentioned in Kojima (2012), the pass-through coefficients in Uganda are low because the country experienced acute fuel shortages in January 2009, pushing up prices. The limitations of the methodology approached to compute these coefficients are discussed in detail in Kojima (2012, 6–8).
- Gruber, J.W., and S.B. Kamin. 2012. “Fiscal Positions and Government Bond Yields in OECD Countries.” _Journal of Money, Credit and Banking_ 44:1563–1587.

- Hansen, B.E. 1996. “Inference When a Nuisance Parameter Is Not Identified Under the Null Hypothesis.” _Econometrica_ 64:413–430.

- —. 1999. “Threshold effects in non-dynamic panels: Estimation, testing and inference.” _Journal of Econometrics_ 93:345–368.

- Hoshi, T., and T. Ito. 2014. “Defying gravity: can Japanese sovereign debt continue to increase without a crisis?” _Economic Policy_ 29:5–44.

- Ichiue, H., and Y. Shimizu. 2015. “Determinants of long-term yields: A panel data analysis of major countries.” _Japan and the World Economy_ 34-35:44–55.

- Jaramillo, L., and A. Weber. 2013. “Bond yields in emerging economies: It matters what state you are in.” _Emerging Markets Review_ 17:169–185.

- Joyce, M.A., A. Lasaosa, I. Stevens, and M. Tong. 2011. “The Financial Market Impact of Quantitative Easing in the United Kingdom.” _International Journal of Central Banking_ 7:113–161.

- Joyce, M.A., Z. Liu, and I. Tonks. 2017. “Institutional Investors and the QE Portfolio Balance Channel.” _Journal of Money, Credit and Banking_ 49:1225–1246.

- Lagarias, J.C., J.A. Reeds, M.H. Wright, and P.E. Wright. 1998. “Convergence Properties of the Nelder–Mead Simplex Method in Low Dimensions.” _SIAM Journal on Optimization_ 9:112–147.

- Laubach, T. 2009. “New Evidence on the Interest Rate Effects of Budget Deficits and Debt.” _Journal of
affect tax morale.

# **Research Design**

In order to contribute to a better understanding of tax morale, we conduct a global online survey experiment of tax morale attitudes. At the empirical core of the study, we use an experiment to estimate the effects of two interventions on tax morale, relative to a control group: First, we look at the effects of a bottom-up participation treatment, where we ask respondents to state their spending preferences. As discussed above, treatments eliciting spending preferences have been found to increase tax morale in some experimental literature, though much of the research here has been confined to specific political and cultural contexts. The breadth of our global survey experiment allows us to get a better sense of the degree to which this type of intervention actually affects tax morale.

Second, in order to test the predictions of a theory of retributive justice, we look at whether a “top-down accountability” treatment, which increases the salience of anti-corruption agencies and the punishments they inflict on corrupt officials, can improve tax morale.

To implement this test, we randomly vary a portion of the survey between the three conditions as laid out in Table 1.

Table 1: Treatments

**<u>Control group</u>**

|**Top-down accountability**|**Bottom-up participation intervention**|
|---|---|
|**intervention**||

6
|
||19-26%|U.S. retail food|2006 to 2008|
|Glauber[25]|23-31%|commodities|Apr 2007 to Apr 2008|
||10%|global food index|Apr 2007 to Apr 2008|
||4-5%|U.S. retail food|Jan to April 2008|
|CEA[42]|35%|corn|Mar 2007 to Mar 2008|
||3%|global food index|Mar 2007 to Mar 2008|
|Rajagopal et al. [54]|15-28%|global cornprice|2007 to 2008|
||10-20%|global soy price|2007 to 2008|
|Hoyos and<br>Medvedev[16]|6%|global food index|2005 to 2007|

Abbott, Hurt, and Tyner, through a review of several reports on the food crisis, conclude that there are several key drivers of food commodity price increases: the depreciation of the dollar, global changes in production such as weather shocks, changes in patterns of food consumption, and the role of biofuels in commodity price increases [2]. They do not, however, present quantitative estimates of percentage contribution to the total price rise that is attributable to a specific factor such as biofuel consumption. The FAO in its State of Food and Agriculture 2008 Report also states that growing demand for biofuels is only among several factors driving 7