rafmacalaba/datause-encoder-data huggingface dataset:
The remainder of the paper is organized as follows. Section II presents the data and the empirical framework. Section III presents our main results. Section IV discusses robustness checks and extensions. Section V concludes.

# **II. Data and Empirical Framework**

This section describes the data and sources used in the empirical analysis. The section also presents the empirical framework used to explore the effect of the COVID-19 oil price crash on the stock returns of U.S. oil firms.

# **II.1 Data**

The daily data for oil and stock prices used in the analysis covers January 2, 2020, to June 30, 2020, for 43 U.S. oil firms. Daily crude oil prices are obtained from the U.S. Energy Information Administration. We use oil prices for the Western Texas Intermediate (WTI) benchmark. Daily data on stock prices for U.S. oil firms are obtained from Compustat. The list of U.S. firms used in the empirical analysis is shown in Appendix Table A1.

Data for debt and other firm characteristics are from 2019, meaning they are _pre-determined_ before the period of analysis. Debt data are obtained from the Compustat database. The debt variables used in the analysis include total debt, long-term debt, short-term debt, and cash as shares of assets. Debt variables are measured in the last quarter of 2019—before COVID-19 was discovered. Shortterm debt/assets is the ratio of current liabilities to total assets (DLC/AT). Current liabilities are those that mature within a year. Long-term debt/assets is the ratio of total long-term debt to total assets (DLTT/AT). Long-term debt is the liabilities with maturities longer than one year. Finally, total debt/assets is the ratio of total debt, including short-term and long-term debt, to total assets ((DLC+DLTT)/AT).

Other firm characteristics used as controls include the logarithm of the firm’s assets, beta, market-to-book ratio, momentum, and profitability. The logarithm of the firm’s assets, market-to-book ratio, and profitability are also from Compustat, measured in the last quarter of 2019. The market-tobook ratio is the ratio of the market value of equity at year-end 2019 to the book value of equity (CEQ). The latter equals the last observation of daily close price (PRCCD) at year-end 2019 multiplied by the number of shares outstanding (CSHOC). Profitability is the ratio of gross profit to total assets (GP/AT). Beta is captured by the slope parameter of the regression of excess daily returns on the daily market logarithm of excess returns from January 1, 2019, to December 31, 2019. Finally, momentum is the exponent of the sum of the daily logarithm of excess returns in 2019.

Table 1 provides basic summary statistics. Between January 2, 2020, and June 30, 2020, the average change in daily oil price is -0.12 percent, and the average change in stock prices is -0.37 percent. Note that April 20 and 21 are dropped from our analyses because of extreme values of log change in oil prices (WTI oil price was negative on April 20). At the year-end of 2019, total debt is on average about 27 percent of assets. Most oil firms’ debt is long-term (26.3% of total assets).

4
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19 Table 3: Rent Sharing Estimates as Proportion of Potential Rent

|||License|Potential||
|---|---|---|---|---|
|Apparel||Price|Rent|Rent|
|Group|Year|(in US$)|(in US$)|Share|
|1. Dresses|1981<br>|...|...|...|
||1982||||
||1983|4.01|13.80|0.710|
||1984|3.82|13.55|0.718|
||1985|1.42|11.78|0.879|
||1986|4.37|14.66|0.702|
||1987|3.41|15.45|0.779|
||1988|1.92|14.77|0.870|
|2. Skirte|1981|...|...|...|
||1982|...|||
||1983|1.47|9.21|0.840|
||1984|1.12|8.86|0.874<br>|
||1985|2.91|10.40|0.720|
||1986|8.54|16.28|0.475|
||1987|9.10|17.15|0.469|
||1988|3.91|12.52|0.688|
|3. Playsuits|1981|...|...|...|
||1982<br>||...|...|
||1983|...|...|...|
||1984<br>|...|...|...|
||1985|...|...||
||1986|0.23|2.79|0.917|
||1987|0.32|3.69|0.913|
||1988|0.23|3.60|0.936|
|4. Sweaters|1981||||
||1982|1.98|6.60|0.700|
||1983|4.35|9.15|0.525|
||1984|1.98|6.97|0.716|
||1985|1.39|6.01|0.769|
||1986|2.63|7.81|0.663|
||1987|1.98|8.41|0.764|
||1988|1.30|7.60|0.829|
|5. Trousers|1981||||
||1982|0.42|...||
||1983|1.89|7.69|.0.754|
||1984|1.86|7.47|0.751|
||1985|1.27|7.01|0.819|
||1986|1.72|1.40|0.767|
||1987|3.03|8.83|0.657|
||1988|1.94|8.30|0.766|
6 through official banking channels. Since a large (and unknown) proportion of remittance monies is transmitted through private, unofficial channels, the level of remittances recorded by the IMF underestimates the actual flow of remittance monies returning to labor-exporting countries.<sup>11</sup> The remittance figures used in this paper therefore underestimate the actual level of international remittances – official and unofficial – received by individual countries.

Finally, with respect to poverty, many developing countries – especially the smaller population countries -- have not conducted the type of nationally-representative household budget surveys that are needed to estimate poverty. For example, of the 157 developing countries classified as low- or middle-income by the World Bank,<sup>12</sup> only 81 countries (52 percent) have published the results of any household budget survey. Of these 81 developing countries, missing data on income inequality reduced the size of the data set used in this paper to 74 countries.

Annex Table A1 gives the countries, regions, poverty, inequality, migration and remittances indicators included in the new data set. The data set includes a total of 190 observations from the 74 developing countries; an observation is any point in time for which data on all the relevant variables exist. The data set is notable in that it includes 42 observations (from 21 countries) in Sub-Saharan Africa, a region for which migration and poverty data are relatively rare. It also includes observations from countries in all other regions of the developing world.
were ultimately linked by the inclusion of an unconditional nondiscrimination clause. Apparently because trade diversion associated with high tariffs was costly, the French negotiated numerous such agreements.

More recently, the role of trade diversion in promoting external trade liberalization has been confirmed by Bohara et al. (2004), who examine the influence of imports from Brazil on Argentina’s external tariffs under Mercosur. Using a cross-industry dataset on Argentina for 1992, 1993 and 1996, they find that increased preferential imports vis-à-vis the value added of the domestic industry led to lower external tariffs in Argentina. Furthermore, the reduction was steeper precisely in the industries that experienced most trade diversion—just as Richardson’s (1993) insight suggests. Bohara et al. concentrate on the effects of increases in preferential imports but do not address the direct effect of preferential tariffs. It is also unclear whether their results capture the effect of Mercosur moving from being an FTA to a CU in 1995.

Estevadeordal et al. (2008) offer the first empirical assessment of the effect of preferential tariffs on external trade liberalization in a large group of developing countries. They study ten Latin American countries, where regionalism forces have been particularly strong, from 1990 to 2001. The RTAs they analyze display heterogeneity both across and within blocs. For example, in a typical RTA there are sectors where no preferences are granted, sectors where partial preferences are offered, and sectors where there is free intra-bloc trade. Preferences also vary significantly over time. Estevadeordal et al. ask whether industries with large preferences have been liberalized to the same extent as other sectors. They find no evidence that trade preferences lead to higher external tariffs or smaller tariff cuts, but find strong evidence that preferences induce a faster decline in external tariffs in free trade areas. The magnitudes imply 19
23

# <u>Notes</u>

1 Dilip Ratha, “Workers Remittances: An Important and Stable Source of External Development Finance,” in World Bank, Global Development Finance (Washington, DC: 2003), p. 157.

2 See, for example, Richard Adams, Jr., The Effects of International Remittances on Poverty, Inequality and <u>Development in Rural Egypt, Research Report 86, (Washington, DC: International Food Policy Research</u> Institute, 1991); Oded Stark, The Migration of Labor (Cambridge: Harvard University Press, 1991); Bjorn Gustafsson and Negatu Makonnen, “Poverty and Remittances in Lesotho, Journal of African Economies 2:1 (March 1993): 49-73; Richard Adams, Jr., “The Economic and Demographic Determinants of Migration in Rural Egypt,” Journal of Development Studies 30:1 (October 1993): 146-157; and J. Edward Taylor, Carol Zabin and Kay Eckhoff, “Migration and Rural Development in El-Salvador: A MicroEconomywide Perspective,” North American Journal of Economics and Finance 10:1 (1999): 91-114.

> 3Charles Stahl, “Labor Emigration and Economic Development,” International Migration Review 16 (Winter 1982): 883.

4 Michael Lipton, “Migration from Rural Areas of Poor Countries: The Impact on Rural Productivity and Income Distribution,” World Development 8 (January 1980): 227.

5 Stark, Migration of Labor, p. 140.

> 6 Adams, Jr., The Effects of International Remittances on Poverty, pp. 73-74.

7 Low income and middle income countries are those which are classified as such by the World Bank in the <u>World Development Report, 2000/01 (Washington, DC), p. 334. Low income includes countries with 1999</u> GNP per capita $756 or less; middle-income includes countries with 1999 GNP per capita of $756 to $9,265.

8 In line with other cross-national studies of poverty, 1980 was selected as a cutoff point because the poverty data prior to that year are far less comprehensive. See, for example, Martin Ravallion and Shaohua Chen, “What Can New Survey Data Tell Us About Recent Changes in Distribution and Poverty,” World <u>Bank Economic Review</u> 11:2 (May 1997): 357-382; and Richard Adams, Jr., Economic Growth, Inequality <u>and Poverty: Findings from a New Data Set,</u> World Bank Research Working Paper 2972, Washington, DC, February 2003.

9 For the purposes of this study, OECD (Europe) includes 21 countries: Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Luxemburg, Netherlands, Norway, Poland, Portugal, Slovak Republic, Spain, Sweden, Switzerland and United Kingdom.

10 The International Monetary Fund (IMF) records annual flow in international remittances in its publication, Balance of Payments Statistics Yearbook (Washington, DC).

11 A recent IMF study estimated that informal transfers of remittance monies currently amount to $10 billion per annum. See Mohammed el-Qorchi, Samuel Maimbo and John Wilson, “The Hawala Informal Funds Transfer System: An Economic and Regulatory Analysis,” unpublished paper, International Monetary Fund, Washington, DC, August 2002, p. 64.

12 For a full list of these 157 developing countries, see World Bank, World Development Report, 2000/01 (Washington, DC), p. 334.
**Table 8** : Cross-section regression estimates (OLS) of the effect of per capita income, trade, FDI net inflows on the occupational gender wage gap by occupational skill level and country income with control variables (standard errors in parentheses)

||**1**<br>**Low an**|**2**<br>**d lower midd**|**3**<br>**le income co**|**4**<br>**untries**|**5**<br>**High an**|**6**<br>**d higher mid**|**7**<br>**dle income c**|**8**<br>**ountries**|
|---|---|---|---|---|---|---|---|---|
|Log GDP per capita|0.04|0.03|0.06|0.03|-0.01|-0.01|0.01|-0.002|
||(0.02)|(0.02)|(0.05)|(0.02)|(0.01)|(0.01)|(0.02)|(0.01)|
|Trade (% GDP)|||||||||
|* Low skill|-0.001|-0.002|-0.001|-0.001|-0.0006|-0.0007|-0.00001|-0.001|
||(0.0003)|(0.0005)|(0.001)|(0.0003)|(0.0002)|(0.0002)|(0.0004)|(0.0002)|
|* High skill|-0.0002|-0.001|0.001|0.0001|-0.0002|-0.0002|0.0004|-0.0002|
||(0.0003)|(0.001)|(0.001)|(0.0003)|(0.0002)|(0.0002)|(0.0004)|(0.0001)|
|Union density* (in %)|-0.001||||-0.0005||||
||(0.002)||||(0.0001)||||
|Wage and Price Regulations|0.01||||-0.05||||
|(scale 1-5)|(0.01)||||(0.01)||||
|Area (log)||-0.03||||0.006|||
|||(0.01)||||(0.003)|||
|Population (log)||-0.003<br>(0.01)||||0.01<br>(0.003)|||
|Number of firms (‘000)|||0.0004<br>(0.002)||||0.001<br>(0.0004)||
|Occupational segregation|||-0.06||||0.01||
|(scale 0-1)|||(0.13)||||(0.02)||
|Wage inequality||||-0.04<br>(0.08)||||0.05<br>(0.04)|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Occupation dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Dummies Cyprus, Japan and|N/A|N/A|N/A|N/A|Yes|Yes|Yes|Yes|
|Korea|||||||||
|Dummy for (ex)Communist|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations<br>|1400|1766|324|1829|6955|7030|2104|7093|
|Adjusted R<sup>2</sup>|0.21|0.20|0.14|0.20|0.59|0.60|0.72|0.56|

Note: standard errors are corrected for clustering within the same country/occupation observations. NA = not applicable.

*For OECD and (ex-)communist countries union density is replaced by collective bargaining coverage and zero respectively.

43
# **Table B1: Data sources and summary statistics**

|Variable|Descriptions|Mean|Standard<br>deviation|Min|Max|
|---|---|---|---|---|---|
|**_Main outcomes_**||||||
|Employment status|= 1 employed in a paid job in the last seven days; 0 = otherwise|0.455|0.498|0.000|1.000|
|Labor income<br>|Weekly (real) income from all jobs in $1,000 AUD.|0.716|0.429|0.000|4.598|
|**_Other outcomes_**||||||
|Labor force participation|= 1 if employed in a paid job in the last seven days, or looked for a paid job in|||||
|(LFP)|the past four weeks, 0 otherwise|0.418|0.493|0.000|1.000|
|Permanent job|= 1 if working on permanent job, 0 otherwise|0.163|0.370|0.000|1.000|
|Skilled occupation|= 1 if having a skilled occupation, 0 otherwise|0.135|0.342|0.000|1.000|
|Agriculture|= 1 if working in agricultural sector, 0 otherwise|0.746|0.435|0.000|1.000|
|Number of work hours per<br>week|Total number of work hours per week|32.654|13.876|1.000|125.000|
|Life satisfaction|0 (Completely dissatisfied) – 10 (Completely satisfied)|7.597|2.120|0.000|10.000|
|Partner labor force|= 1 if partner was employed in a paid job in the last seven days, or looked for a|||||
|participation|paid job in the past four weeks, 0 otherwise|0.200|0.400|0.000|1.000|
|Partner life satisfaction|0 (Completely dissatisfied) – 10 (Completely satisfied)|7.608|2.014|1.000|10.000|
|Child mental health|Kessler 6 total score of children|8.954|5.323|0.000|29.000|
|Child education performance<br>below average|= 1 if overall achievement at school is below average, 0 otherwise|0.061|0.240|0.000|1.000|
|**_Mental health measures_**||||||
|Mental health|Kessler 6 Total Score|11.695|5.513|6.000|30.000|
|Mental health - nervous|Feeling nervous in the last four weeks (1. None of the time; 2. A little of the<br>time; 3. Some of the time; 4. Most of the time; 5. All of the time).|2.101|1.151|1.000|5.000|
|Mental health - hopeless|Feeling hopeless in the last four weeks (1. None of the time; 2. A little of the<br>time; 3. Some of the time; 4. Most of the time; 5. All of the time).|1.825|1.117|1.000|5.000|
|Mental health - restless|Feeling restless or fidgety in the last four weeks (1. None of the time; 2. A little<br>of the time; 3. Some of the time; 4. Most of the time; 5. All of the time).<br>Feeling that everything was an effort in the last four weeks (1. None of the time;|1.960|1.165|1.000|5.000|
|Mental health - effort|2. A little of the time; 3. Some of the time; 4. Most of the time; 5. All of the<br>time).|2.426|1.430|1.000|5.000|

54
Figure 3 shows that the age distribution of male NEETs did not change substantially between 2007 and 2018, as it did for females, especially for younger ones. The rapid increase in enrollment rates of young women in secondary and tertiary education explains this marked difference. However, when the same females approach the age at which they would normally be entering the labor market—their early 20s—this positive improvement they experienced in their early educational enrollment rates has all but been erased. The NEET rate for females age 23 to 24 is virtually the same in 2018 as it was in 2007, more than a decade earlier.

**Figure 3: Percentage of the Moroccan Population Who are NEET: Males and Females by Age**

<!-- Start of picture text --> 80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>15 16 17 18 19 20 21 22 23 24<br>Age<br>Males, 2007 Females, 2007<br>Males, 2018 Females, 2018<br><!-- End of picture text --> _Source:_ authors’ own elaboration based on the _Enquête nationale sur l_ ’ _emploi_ (ENE).

Figure 4 focuses on 2018 only but provides a more comprehensive snapshot of labor market outcomes by age and gender. We also represented the shares of those in education or working: the differences between men and women are quite striking. The disadvantaging of females starts very early on. In 2018, just 19 percent of girls aged 15 were NEET compared to more than 30 percent in 2007, a marked improvement. In 2018, of the remaining 81 percent of 15-year-old girls, 77 percent were in school and 4 percent were working. At 24 years old, however, more than 70 percent of women in 2018 were NEET, compared to just 19 percent of 15-year-olds.

By contrast, in 2018 about 5 percent of boys aged 15 were NEET—not much different from the 7 percent or so who were NEET in 2007. In comparative terms, that 5 percent figure for NEET young boys is not hugely different from the 19 percent of 15-year-old girls who were NEET in 2018. What is more significant is that when men reached 24 years old in 2018, about 21–22 percent of them were 11
Hence, one important question that arises is: _when_ should impacts be measured? By using qualitative methods to understand the context and by drawing on a range of experiences elsewhere, evaluators can derive informed knowledge of firms’ behavior and motivations, and thus help to more accurately specify what outcomes the intervention can be expected to generate over a given time-frame. Integration of methods at this stage might prove beneficial for investment climate reforms given that assessments such as the Doing Business indicators or the Enterprise surveys can identify potential areas of reform. However, few details are provided regarding what and why firms need these reforms. Hence qualitative work can contribute to refining the intervention.

# _<u>Evaluation design</u>_

Qualitative analysis and data collection can complement quantitative techniques in the evaluation design to address common challenges such as identification and inference of causal relations:

**a** ) **Identification:** Qualitative data collection and analysis can be helpful in informing and selecting samples (whether of people, places or issues) of interest. For example, in-depth interviews or focus groups might be used to identify firms or individuals with “entrepreneurial” behavior, or to identify what constitutes entrepreneurial behavior according to the context and prevailing social norms. Once firms are identified, quantitative methods can be applied to the population of interest to make the sample (more) representative. Another common identification strategy is selecting samples (or even stratified samples) of interest from the sampling list with specific characteristics; qualitative research can then be conducted on those selected individuals or units of interest to help explain common or different characteristics, or to explain variance or outlier behavior (Tedlie et al, 2007). This technique is particularly useful when sample sizes are small.

Qualitative data collection methods have also been useful in refining the identification strategy and diminishing the risk of selection bias, especially for quasi-experimental studies where it is difficult to control for unobservable variables. An example is Bloom et al (2013), who assessed the impact of management practices in firms’ performance in India by conducting retrospective interviews and observation assessments at the factories of a representative sample of firms. Data gathered was used to confirm that there was no significant difference between the project and 12
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18