rafmacalaba/datause-encoder-data huggingface dataset:
even after controlling for several firm-level and country-level variables. The relationship is quantitatively large. For instance, for the baseline specification, our most conservative estimate suggests that moving from a country of the size of Namibia (25<sup>th</sup> percentile value of population) to a country of the size of Morocco (75<sup>th</sup> percentile value of population) is associated with an increase in the level of overall corruption by 0.28 percentage point or about 23 percent of its mean value. This is an economically large increase.

The plan of the remaining sections is as follows. In section 2, we describe the data and the variables used in the regressions. Our empirical results for overall corruption and petty corruption are provided in section 3. We summarize the main findings in the concluding section and suggest scope for future work.

# **2. Data and main variables**

In this section, we discuss the data. The main data source we utilize is firm-level survey data collected by the World Bank’s Enterprise Surveys (ES). The ES are nationally representative surveys of the non-agricultural and non-financial private economy. A common sampling methodology – stratified random sampling – is followed in all the surveys along with a common questionnaire.<sup>4</sup> The sample for each country is stratified by industry, firm-size, and location within the country. Weights are provided in the survey and used in all our regressions so that the sample is representative of the target population.

Our main regression results are based on a sample of 47,952 firms in 135 countries.<sup>5</sup> Of these, 26,526 are manufacturing firms while the remaining 21,426 firms belong to the services

> 4 Details of the sampling methodology and other survey related information are available at <u>www.enterprisesurveys.org.</u>

> 5 The list of countries is provided in Table A1 in the Appendix. All the countries covered by ES are included in this study.

7
and business activities are characterized by strong productivity growth in the majority of ECA countries during 1997-2004.<sup>20</sup> For example, annual growth in WIIW labor productivity in financial intermediation averages 11.3% across all countries and years. Third, there is large variation in average annual growth rates of WIIW labor productivity between 1997-2000 and 2000-2004. In particular, productivity growth in electricity, gas, and water, in wholesale, retail, and repair of motor vehicles, and in financial intermediation accelerates in most countries in 2000-2004 relative to 1997-2000.<sup>21</sup> Fourth, despite the overwhelming evidence in Figure 10 of strong productivity growth, a few exceptions occur: (i) the Slovak Republic experiences an average annual decline of labor productivity in hotels and restaurants of more than 10% in 2000-2004, (ii) Bulgaria and Romania exhibit productivity declines in wholesale, retail, and repair of motor vehicles averaging 4.6% in 1997-2000 and 6% in 2000-2004, respectively, and (iii) Romania and the Slovak Republic display average annual productivity declines in financial intermediation of 3.5% and 10.9% in 1997-2000, respectively.

Comparing growth rates of WIIW labor productivity across sub-sectors is subject to the problem that GDP deflators used to deflate real value added account imperfectly for changes in sub-sector specific domestic and international output prices. Consequently, the labor productivity growth rates of some sub-sectors may be over-estimated while those of others may be under-estimated, depending on the evolution of the expenditurebased PPP conversion factors relative to the evolution of the production-based PPP conversion factors. Appendix Table B.1 shows for EU-5 countries - included in the WIIW and the KLEMS databases - growth in the expenditure-based PPP conversion factors and growth in the production-based PPP conversion factors for some sub-sectors from 1997 to 2004. It is very clear that the changes over time in domestic and international overall expenditure prices differ significantly from changes over time in domestic and international output prices across sub-sectors.

> 20 The small or negative growth in labor productivity of real estate, renting and business activities may be associated with a stronger increase in employment than in value added of real estate activities as employment in the sub-sector took longer to respond to the real estate boom. 21 Poland is an exception to this pattern for financial intermediation since its average annual growth in WIIW labor productivity is much stronger in 1997-2000.

22
In equation (4), ∆BankDDi,t is the change in Merton distance-to-default variable for bank _i_ in week _t._ ∆SystemDDt is similarly the change in the value-weighted Merton distance-todefault variable for all banks in a given country. Mt−1are lagged state variables and include change in the term spread ( _TERM_ ), change in the default spread ( _DEF_ ), CBOE implied volatility index ( _VIX_ ), S&P 500 return ( _SPRET_ ) and the change in the 3 month t-bill rate ( _RATE_ ). The ΔCoVar variable is then computed as the change in the VaR of the system when the institution is at the _q_<sup>th</sup> percentile (or when the institution is in distress) minus the VaR of the system when the institution is at the 50% percentile: We compute the ΔCoVar measure for each bank in our sample for the pre-crisis and postcrisis time periods separately, and higher ΔCoVar indicates lower systemic risk.

# **2.3. Country Level Variables**

Country level variables are collected from a number of sources. We obtain a deposit insurance variable from Barth, Caprio, and Levine (2008). Specifically, deposit insurance is equal to 1 if a country has explicit deposit insurance and depositors were fully compensated the last time a bank failed and 0 otherwise. We obtain a second deposit insurance variable from Demirguc-Kunt, Kane, and Laeven (2008). Full coverage is equal to 1 if a country offers full coverage and 0 otherwise. Since deposit insurance schemes are often changed during times of crises and we are interested in the impact of deposit insurance on bank risk during the recent precrisis and crisis periods, we take the deposit insurance coverage in existence in 2003 as our independent variable and use bank-year observations from 2004 to 2009 in our study to mitigate the reserve causality problem. Although fixing the deposit insurance measure at year 2003 leads to measurement error in the deposit insurance variable and may bias us against finding any significant results, it alleviates the concern that some unobservable country characteristics drive both changes in deposit insurance and bank risk.<sup>4</sup>

> 4 It might be argued that the deposit insurance scheme in 2003 is positively correlated with government interventions in the recent crisis, which seems to favor our hypothesis that deposit insurance is associated with lower bank risk in the crisis. However, there is no evidence for such a claim and in contrast previous studies have suggested that lack 9
39

## TABLE 5

# COMPARISON OF ESTIMATES OF FACTOR SUBSTITUTION ELASICITY AND FACTOR DEMAND ELASTICITY

||Berndt<br>& Wood<br>(U.S.A.)|Gnffen&<br>Gregory<br>(U.S.A.)|Pindyck_;/_|Bendt&<br>Mormison<br>(U.S.A.)|Christensen<br>& Greene_h/_<br>(U.S.A.)|Our<br>Study|
|---|---|---|---|---|---|---|
||1.01|.06|.06to.52||.63||
|LE|.65|.87|.721O .87||.16|1.42/1.98|
|'KE|-3.22|1.07|1.02 to 1.07||.21||
|'ME|.75|||||1.03/1.11|
|'EE|_-.045_|-.79|-.78 to -.80|_-.055_|-.08|-.941-.98|
|I'LL|-.46|-.12|-.12 to -.27|-.34|-.22|-.39/-.54|
|'KK|-.50|-.18|-.18 to -.38|-.20|-.23||
|"MM|-.24|||-.22||-.16/-.22|
|'EL|.18|.64||.42||-.23/-.26|
|'EK|-.18|.15||-.11|||
|'EM|.46|||.23||.741.69|
|ILE|.03|.11|.11|.07||.00/.02|
|| 1 KE|-.14|.13|.13|-.07|||
|'ME||||.01||.00/.02|

**a/** Nine industral **countries.**

**b/** U.S. electric power generation, E stands for fuels in their study.
**References**

Behrman, Jere and Nancy Birdsall. 1983. "The Quality of Schooling: Quantity Alone is Misleading, _" American Economic Review_ 73(5): 928-946.

Berger, Jacqueline. 1995. "Were you Referred by a Man or a Woman? Gender of Contacts and Labor Market Outcomes," Working Paper 353, Industrial Relations Section, Princeton University.

Blinder, Alan S. 1973. "Wage Discrimination: Reduced Form and Structural Estimates," _The Journal of Human Resources_ 8: 436-455.

Cotton, Jeremiah. 1988. "On the Decomposition of Wage Differentials," _Review of Economics and Statistics_ 70: 236-243.

Griliches Zvi and William Mason. 1972. "Education, Income and Ability," _Journal of Political Economy_ 80(3): S74-S 103.

Hersch, Joni and W. Kip Viscusi. 1996. "Gender Differences in Promotions and Wages," _Industrial Relations_ 35(4): 461-472 Jones, David and Gerald Makepeace. 1996. "Equal Opportunities: Pay and Promotion in an Internal Labour Market," _The Economic Journal_ 106: 401-409.

Lazear, Edward P. and Sherwin Rosen. 1990. "Male-Female Wage Differentials in Job Ladders," _Journal of Labor Economics_ 8(1): S106-S123.

Neumark, David. 1988. "Employers' Discriminatory Behavior and the Estimation of Wage Discrimination," _The Journal of Human Resources_ 23(3): 279-295.

Oaxaca, Ronald L. 1973. "Male-female Wage Differentials in Urban Labor Markets,' _International Economic Review 9:_ 693-709.

Oaxaca, Ronald L. and Michael Ransom. 1994. "On Discrimination and the Decomposition of Wage Differentials," _Journal of Econometrics_ 61: 5-21.

Oaxaca, Ronald L. and Michael Ransom. 1993. "Salary and Promotion Differences Between Gender and Nationality Groups for Specific Grade Levels," prepared for the World Bank, April.

35
might be an income supplementation strategy next to on-farm self-employment. Following Quiñones et al. (2009) we define a person to be full-year employed if (s)he works at least 10 months during the past 12 months and to be full-time employed if (s)he works at least 35 hours per week during an average working week. This results in four classes: 1) full-year/full-time employed, 2) full-year/part-time employed, 3) part-year/full-time employed (so-called seasonal employment), and 4) part-year/part-time employed (so-called casual employment). The Nigeria GHS-Panel, Tanzania NPS and Uganda NPS questionnaires do not ask questions on the hours worked in non-farm enterprises, so for these countries we only distinguish full-year and part-year employment.

Finally, we construct a variable that captures the dynamic nature of off-farm employment status over time. Based on the employment status in each round of data collection, we consider people to be either never employed, to drop out of employment, to start with employment or to continue with employment. In the case of Ethiopia and Uganda, where we have more than two panel survey rounds at our disposal, we compute this variable based on the last two survey rounds.

# **4. Off-farm employment: Who participates in which jobs?**<sup>**10**</sup>

# _4.1. Participation rates_

Figure 1 and Table 1 present participation rates in off-farm employment across gender and space for Ethiopia, Malawi, Nigeria, Tanzania and Uganda. The share of the working-age population that is involved in off-farm employment is quite high and cannot be considered as negligible. In the latest survey round this is 34 percent in Ethiopia, 40 percent in Uganda, 47 percent in Nigeria, 52 percent in Tanzania and 58 percent in Malawi, which corresponds to a cross-country weighted overall share of 44 percent. These shares imply that approximately 68 million people are off-farm employed across these five countries (Figure 2). Off-farm employment rates are consistently higher in urban areas (ranging from 53 percent in Uganda to 73 percent in Malawi) than in rural areas (ranging from 24 percent in Ethiopia to 54 percent in Malawi), which is not surprising given

> 10 Unless otherwise stated, the country-specific differences in the estimates that are reported in this section by gender, age category, rural/urban location, and time are statistically significant at least at the 10 percent level, as reported in Tables 1-3.

8
Figure A10: Specification Charts for Percentage of Days with Rain

|-1<br>0<br>1<br>2<br>3<br>p < 0.05<br>n.s.<br>p < 0.05<br>Ethiopia<br>-4<br>-2<br>0<br>2<br>4<br>p < 0.05<br>n.s.<br>p < 0.05<br>Malawi|
|---|
|-2<br>ERA5<br>TAMSAT<br>***Weather Product***<br>ERA5<br>TAMSAT<br>***Weather Product***|
|CHIRPS<br>CPC<br>MERRA-2<br>ARC2<br>CHIRPS<br>CPC<br>MERRA-2<br>ARC2|
|Quantity<br>Value<br>***Dep. Var.***<br>Quantity<br>Value<br>***Dep. Var.***|
|Wth + FE<br>Weather + FE + Inputs<br>***Model***<br>Wth + FE<br>Weather + FE + Inputs<br>***Model***|
|Weather<br>eaer<br>Weather<br>eaer|
|0<br>4<br>8<br>12<br>16<br>20<br>24<br>28<br>32<br>36<br>0<br>4<br>8<br>12<br>16<br>20<br>24<br>28<br>32<br>36|
|-5<br>0<br>5<br>p < 0.05<br>n.s.<br>p < 0.05<br>Niger<br>-2<br>-1<br>0<br>1<br>2<br>3<br>p < 0.05<br>n.s.<br>p < 0.05<br>Nigeria|
|***Weather Product***<br>***Weather Product***|
|TAMSAT<br>TAMSAT|
|ARC2<br>ERA5<br>ARC2<br>ERA5|
|MERRA-2<br>MERRA-2|
|CHIRPS<br>CPC<br>CHIRPS<br>CPC|
|Value<br>***Dep. Var.***<br>Value<br>***Dep. Var.***|
|Quantity<br>Quantity|
|<br>Weather + FE + Inputs<br>***Model***<br><br> <br>Weather + FE + Inputs<br>***Model***<br>|
|Weather<br>Weather + FE<br>Weather<br>Weather + FE|
|0<br>4<br>8<br>12<br>16<br>20<br>24<br>28<br>32<br>36<br>0<br>4<br>8<br>12<br>16<br>20<br>24<br>28<br>32<br>36|
|<br> <br>Tanzania<br> <br> <br>Uganda|
|-1<br>0<br>1<br>2<br>***Weather Product***<br>p < 0.05<br>n.s.<br>p < 0.05<br>-2<br>0<br>2<br>4<br>***Weather Product***<br>p < 0.05<br>n.s.<br>p < 0.05|
|TAMSAT<br>TAMSAT|
|ARC2<br>ERA5<br>ARC2<br>ERA5|
|CPC<br>MERRA-2<br>CPC<br>MERRA-2|
|CHIRPS<br>CHIRPS|
|Value<br>***Dep. Var.***<br>Value<br>***Dep. Var.***|
|Quantity<br><br>Quantity<br>|
|<br>Weather + FE + Inputs<br>***Model***<br> <br>Weather + FE + Inputs<br>***Model***|
|Weather<br>Weather + FE<br>Weather<br>Weather + FE|
|0<br>4<br>8<br>12<br>16<br>20<br>24<br>28<br>32<br>36<br>0<br>4<br>8<br>12<br>16<br>20<br>24<br>28<br>32<br>36|

_Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 36 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

58
various technical programs. The mathematics/language and literature assessments include 19 and 23 items, respectively ( **Table 1** ). We compute the outcome variable as the percentage of correct answers in the EAES in the selected subjects. We then standardize it relative to the control group's mean and standard deviation. In the appendix, we present results based on Item Response Theory (IRT) in order to check the robustness of our results.

# **Enrollment and Repetition**

Other key outcomes of interest include enrollment in the third semester and the probability of repeating at least one subject. The first outcome takes a value of one if a student enrolls in the third semester and zero if otherwise. Similarly, the second outcome takes a value of one if a student repeats at least one subject (up to that semester) and zero if otherwise. Both outcomes originate from available administrative data collected by SENESCYT in the second semester of the 2021 academic year.<sup>5</sup>

# _4.2. Baseline Covariates and Balance Results_

We test whether pre-treatment characteristics differ for the treatment and control groups. These covariates were obtained from the administrative enrollment dataset gathered by SENESCYT in the second semester of the 2020 academic year. This dataset collects comprehensive information from each student that ranges from unique identification (e.g., student ID), basic demographics (e.g., date of birth, gender, ethnicity), proxies of socioeconomic status (e.g., whether the student studies and works), whether the family receives cash transfers and parental education (i.e., the “Bono de Desarrollo Humano”), along with other academic information such as the admission score obtained during their higher education application process.

**Table 2** presents the results, with student-level and institute-level characteristics displayed in the top and bottom panels, respectively. In the control group, students are 22 years old and primarily male (60 percent). About 40 percent combine study and work, and only 2.3 percent receive a

> 5 The variables included in this dataset along with the descriptions and labels are described in the document named “Guía de Registro de Institutos y Conservatorios Superiores Públicos y Particulares Matriculados”. The information is uploaded directly by each TTIs to the National Information System for Higher Education (SNIESE) and transmitted directly by internet.

10
12

# Figure 1. Growth and Export Diversification, 1962–2010

# All Countries Asian Countries

<!-- Start of picture text --> CHN<br>KOR<br>HKG SGP<br>THA MYS VNM<br>N LD F RA I TA A UT D EUU SA GB R C ZE D NK E SP S WE H UN C HE S VN H RV P JPN IND IDN LAO LKA<br>AUS<br>PNGMNG<br>NZL PHL NPL KHMBGD<br>2 2.5 3 3.5 4 4.5<br>Theil Index<br>1 2 3 Theil Index 4 5 6 Frontier Asia Other APD Countries<br>Sources : Penn World Table 7.0, UN COMTRADE, and author calculations.<br>Note : GDPPC = Gross Domestic Product Per Capita. AZE = Azerbaijan, etc. APD = Asia & Pacific.<br>Figure 2. Volatility and Export Diversification<br>4.5<br>4.4<br>4.3<br>4.2<br>4.1<br>4.0<br>3.9<br>3.8<br>3.7<br>3.6<br>Pre-episode Episode Post-episode<br>10 8<br>6<br>5<br>4<br>0 Real GDPPC Growth 2<br>Real GDPPC Growth<br>0<br>-5<br>in percent<br>Volatility of GDP Per Capita,<br><!-- End of picture text --> _Sources_ : UN COMTRADE, and author calculations.

_Note:_ Episode indicates diversification spurts. The procedure for identifying spurts is based on Berg et al. (2012).
17a

# Tab leas MEXaCO MACRO INDICATORS 1980 TO 1989

|NATIONAL ACCOUNTS<br>ODP GROWrH (%)|1980<br>832|1981<br>7.95|1982<br>-0.55|1983<br>-5.27|1984<br>3.67|1985<br>2.78|1986<br>1.97|1987<br>1.S0|<br>198<br> <br>1.10|1989<br>2.89|
|---|---|---|---|---|---|---|---|---|---|---|
|RELATIVE PRICE|||||||||||
|REAL EFFECTVE EXCH'85|93.40|82.00|110.53|124.37|106.14|100.00|132.29|139.40|<br>115.31|107.10|
|BILATERAL REL EXR/I|83.38|75.89|112.11|119.71|103.S8|100.00|124.17|123.90|<br>99.25|93.96|
|NOMINAL VARIABLES (%)|||||||||||
|DEVALUATION (AVG EXR)|0.64|6.81|130.07|112.93|39.75|53.06|138.16|125.28|<br>63.47|8.15|
|WPI INFLATION|24.43|24.52|56.08|107.34|10.33|S3.56|88.39|135.63|<br>107.82|16.08|
|CPI WFLATION|26.31|28.00|58.87|101.88|6S.46|57.75|86.23|131.83|<br>114.20|20.01|
|Ml GROWtH|32.13|33.12|62.36|40.35|59.99|_49.55_|67.24|118.08|<br>67.82|3S.63|
|M2 GROWTH|36.67|49.04|58.04|61.87|68.4|43.25|78.44|147.22|<br>42.20|43.00|
|SEIONORAGE (%) /2|4.68|5.27|10.44|6.47|5.61|1.74|3.45|3.09|1.63|0.41|
|RATIOS|||||||||||
|MI/GDP|10.67|10.36|10.52|8.09|7.86|7.30|7.30|6.54|5.33|5.53|
|M2/GDP|29.02|31.55|31.18|27.66|28.27|25.18|26.84|27.29|18.70|20.60|
|RESERVE MONEY/GDP|16.15|17.05|21.AI|18.04|16.55|12.04|10.64|7.46|S.25|4.43|
|PRIARY DEFICIT/GDP /3|-3.02|-8.03|-3.54|4.84|5.46|3.89|2.58|S.66|7.89|8.29|
|OPERATIONAL DEFICIT/GDP /3|-6.55|-13.01|_-IS.55_|-8.14|-7.15|-8.04|-14.45|-14.40|-9.15|-4.73|
|NTERNATIONAL<br>TRANSACTIONS (S)|||||||||||
|EXPORTS|ISS70|20102|21230|22312|24196|21663|16031|2065S|20566|2276S|
|IMPORTS|18896|23948|14435|85S50|1125S|13212|11432|12222|18898|23410|
|TRADE BALANCE|-3326|-3846|6795|13762|12941|8451|4S99|8433|1668|-64S|
|CURBAL|-8162|-13899|.6218|5419|4239|1237|-1673|3967|-2443|-5449|
|TOTAL CHANGE IN RESERVES|-1079|-1333|3542|-2183|-235S|2972|232|-S684|6789|3658|
|INT RESERVES/IMPORTS<br>/4|6.8|6.8|S.9|17.0|26.3|21.2|18.8|45.8|28.7|12.0|

1/ Against die US dolan.

2/ Defined as chanbe in rseve money over GDP.

3/ World Bank Estinute 41 In weeks of imports, Source: Yeady IFS.
Figure 5: Size vs. firm-level capabilities <!-- Start of picture text --> (a) Size vs. management (b) Digital readiness vs. management<br>3 3<br>2.5 2.5<br>2 2<br>1.5 1.5<br>-.5 0 .5 1 1.5 -1 0 1 2 3 4<br>Normalized management score Initial Digital readiness score<br>Necessity De Soto Parasite Fully formal Necessity De Soto Parasite Fully formal<br>ln(number of employees) ln(number of employees)<br><!-- End of picture text -->

**Notes:** Both panels control for sector, age, and country. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

# **5.3 The severity of the impact of the COVID-19 shock and the recovery speed is negatively associated with the degree of informality**

Using drop in sales across the three waves of data collection as our main outcome variable, we document that sales decreased significantly more for informal firms (an average 45% drop in sales during the first wave compared to 36% for formal firms, controlling for firmlevel attributes) (Figure 6, panel a) and remain significantly below their pre-pandemic sales (17% below). These results are comparable across the various types of informal firms, with the necessity firms being hit slightly harder. Nevertheless, there is a tight negative correlation between the degree of informality and the change in sales, pooling the three waves together (Figure 6, panel b). Similar results are obtained with respect to recovery across waves (∆ _Change in salest,t−_ 1), that is, as the degree of informality increases, recovery is slower (Figure A7).

The external attributes of firms that are associated with informality are also the ones that have been negatively associated with firm performance during the pandemic. For example, micro and small firms witnessed a larger drop their sales and a slower recovery (Cirera et al., 2021). Given that when data are limited such attributes are used as proxy for informality, (e.g., Perry and Maloney (2007) justifies using firm size to indicate informality because "most microenterprises are informal"), we test if the poor performance of firms during crisis is explained by factors in addition to these attributes. To this end, we implement the following approaches: : _Weighted cumulative distribution function_ Specifically for firm size, the differences in cumulative distribution function of change in sales between formal and informal firms persist even 15
31 Table 1. TVP Ownership Structure in Four Counties of China, 1985 (percentage of total gross industrial output value of TVPs)

|Type of TVP|Wuxi|Nanhal a|Jieshou|Shangrao|China|
|---|---|---|---|---|---|
|Township**fiLrs**<br>Vlllage flrms<br>Team fLrms<br>Private firms|48<br>47<br>2<br>3|43<br>31<br>16<br>10|36<br>13<br>-<br>51|43<br>22<br>-<br>35|45<br>38<br>9<br>8|
|Total<br>(millions of<br>yuan)|3,705|1,421|127|31|175,008|

a. Shares in gross revenues of industrial TVPs. Source: Fleldwork of World Bank/Insitltute of Economics of the Chinese Academy of Social Sciences research project.
_5_

# 2. ALLOCATION OF OIL PRICE RISK IN VENEZUELA: GOVERNMENT AND PDVSA

In recent years, the petroleum sector has accounted for about 23% of GDP; howtver, inclusion of its indirect contributions (spillover effects to other sectors) brings its share up to two-thirds of the GDP. Between 1982 and 1988 oil-related revenues (mainly PDVSA's taxes and royalties) accounted for an average of 67.3% of total government revenues. In 1991 the share of oil-related revenues rose to 78.5%. These statistics indicate the high exposure of the <!-- Start of picture text --> Venezueia<br>1.4<br>i 3<br>1.2<br>1.1<br>I<br>c 073<br>0.4<br>L 0.20.<br>w 0. 13<br>-0.2<br>-0.1<br>-0.2<br>-0.3<br>-0.7<br>1971 1973 1975 1977 1979 1981 1993 1985 1987 1989 1991<br>D3 oil tax rev. + Oil prices<br><!-- End of picture text --> Fig=r 1