rafmacalaba/datause-encoder-data huggingface dataset:
diversity measures used by Easterly and Levine are significant when the income variable is excluded from the regressions. However, they are not (nor do they have any explanatory power) when income is included. The variables are negative, suggesting that the manner in which they affect urbanization is by depressing income, which in turn reduces the level of urbanization.

Easterly and Levine also use a measure of racial tensions. Following the same logic as for SIVARD, we expect that racial tensions should result in higher urbanization. Cities tend to be centers where national identity is forged, and are less frequently dominated by one ethnic group. The racial tension data is only available for 1984, but to the extent that this is related to structural issues in a country, we use it as a proxy for racial tensions. over the whole period. The variable has a significantly positive effect on both the level and the rate of urbanization. In addition, it somewhat decreases the coefficient and level of significance of the Africa dummies. Thus, even if we control for level of income or of urbanization, racial tensions help explain higher than expected urbanization.

Regression 11 in Table 2 includes all significant variables,1<sup>3</sup> and shows that given the Africa dummies we can explain almost 90% of cross country variations in levels of urbanization. This specification, however, treats all observations equally and does not allow us to look at a country's individual urbanization process. We therefore run the regressions with country fixed effects, which enables us to determine how well we can explain countries' individual urbanization processes. Table 4 shows these results.

# _Countries' evolution over time: a fixed effect approach_

In the fixed effect specification, shown in Table 4, we find that income and income structure still are strongly correlated with a country's level of urbanization. The results are not substantially different than without fixed effects, although NOEDUC and NOEDUC2, CEREAL or black market premia are never significant. Secondary education and rural-urban wage differentials (RUW) are significant, as well as SIVARD is usually significant, although it is now negative. Thus, while civil strife is correlated with higher urbanization across countries, it tends to reduce a given country's urbanization level. The negative crop shock variable is now significant and negative, suggesting 'that countries that have experienced drops in yields in the previous five years, will tend to have a lower than otherwise expected level of urbanization. This could be due to the fact that rural people become too poor to move after a large reduction in crop yield, but in general the results on the crop shock variables are weak and highly sensitive to sample changes.

> 13 Since civil and political rights data are only available from 1975 onwards, we omitted them from regression 1 1.

19
**Table A1.** Variables associated to each skill measure using the Philippines STEP survey and O*Net <u>database</u>

|Skill|Variable from STEP|Variables from O*Net|
|---|---|---|
||Type of document read|Getting Information|
||Length of longest document typically read<br>Filling forms<br>Math tasks<br>Frequency of thinking for at least 30 minutes to do tasks <br>|Identifying Objects, Actions, and Events<br>Estimating the Quantifiable Characteristics of Products,<br>Events, or Information<br>Judging the Qualities of Things, Services, or People<br> Processing Information<br>Evaluating Information to Determine Compliance with<br>|
||Frequency of learning new things|Standards|
|Non-routine<br>analytical||Analyzing Data or Information<br>Making Decisions and Solving Problems<br>Thinking Creatively<br>Updating and Using Relevant Knowledge<br>Developing Objectives and Strategies<br>Scheduling Work and Activities<br>Organizing, Planning, and Prioritizing Work<br>Drafting, Laying Out, and Specifying Technical Devices,<br>Parts, and Equipment|
|||Interacting With Computers|
||Supervising coworkers<br>Making presentations<br>Contact with people other than coworkers|Interpreting the Meaning of Information for Others<br>Communicating with Supervisors, Peers, or Subordinates<br>Communicating with Persons Outside Organization|
||Frequency of collaborating with coworkers|<br>Establishing and Maintaining Interpersonal Relationships<br>Assisting and Caring for Others<br>Selling or Influencing Others|
|||Resolving Conflicts and Negotiating with Others|
|Non-routine<br>interpersonal||Performing for or Working Directly with the Public<br>Coordinating the Work and Activities of Others<br>Developing and Building Teams<br>Training and Teaching Others<br>Guiding, Directing, and Motivating Subordinates<br>Coaching and Developing Others<br>Provide Consultation and Advice to Others<br>Performing Administrative Activities<br>|
|||Staffing Organizational Units|
|Non-routine<br>manual|Operate with heavy machines<br>Drive a car, truck or three wheeler<br>Repair electronic equipment|<br>Inspecting Equipment, Structures, or Material<br>Operating Vehicles, Mechanized Devices, or Equipment<br>Repairing and Maintaining Mechanical Equipment|
||Physically demanding work|Repairing and Maintaining Electronic Equipment|
|Routine<br>cognitive|Lack of freedom to decide in which way to work|Documenting/Recording Information|
||Frequency of repetitive tasks|Performing General Physical Activities|
|Routine<br>manual||Handling and Moving Objects<br>Controlling Machines and Processes<br>Monitor Processes, Materials, or Surroundings<br>Monitoring and Controlling Resources|

Source: Authors’ elaboration based on Philippines STEP survey 2015-2016 and O*Net database.

57
Table S.3: Balance test across treatment conditions

||Treat|ment A|Treatm|ent B|R-sqd.|No.|
|---|---|---|---|---|---|---|
|Dependent variables:|Coefcient|Std. errors|Coefcient|Std. errors||of obs.|
|**Panel A: Student-leve**|**l variables **|**(one experim**|**ented studen**|**t for one ho**|**usehold**|**)**|
|Age (years)|-0.020|(0.145)|0.098|(0.134)|0.002|882|
|Male (dummy)|-0.045|(0.052)|-0.027|(0.043)|0.000|882|
|Education (years)|0.051|(0.096)|0.037|(0.115)|0.002|882|
|**Panel B: Household-l**|**evel variabl**|**es**|||||
|Household size|0.007|(0.088)|-0.152**|(0.061)|0.003|882|
|No. of males|-0.084|(0.069)|-0.183**|(0.076)|0.004|882|
|Head age (years)|0.175|(0.690)|0.318|(0.699)|0.000|882|
|Head education (years)|-0.159|(0.242)|-0.147|(0.222)|0.001|882|
|Head male (dummy)|0.005|(0.028)|0.019|(0.023)|0.001|882|
|Land size (decimal)|0.495|(2.781)|0.647|(1.837)|0.000|882|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each village.

48
LOW-INCOME **ECONOMIES**

# **1.** Eztimates and Projections

|Tow|pquiat2in|**Sp.**<br>ratio|Period|Growth<br>rate|Ca|CDR|**TFE**|O<br>_Note_|0<br>Femals|**IRE**|qS<br>**xlOO0**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**1905**|2711235|69.2|1985-89|2.04|30.9|10.3|3.88|60.2|61.7|84|118|
|1990|3002970|65.7|1990-94|.. 92|29.0|9.7|3.59|61.4|63.3|75|105|
|1995|**330s546**|65.1|1995l-9|<br>1.77|26.8|9.0|3.33|62.8|65.0|67|93|
|2000|3611290|63.5|2000-04|1.64|24.9|8.4|3.15|64.2|66.9|59|_1_|
|2005|3920688|60.0|2005-09|<br>1.52|23.4|8.1|2.99|65.3|68.2|5i|74|
|2010|4231067|56.5|2010-14|1.43|22.1|7.8|2.U2|66.4|69.5|48|66|
|2015|4543920|54.4|2014-19|1.3i|21.1|7.6|2.70|67.6|71.0|42|57|
|2020|4861230|53.9|2020-24|1.26|20.1|7.4|2.59|68.8|72.6|36|48|
|2025|5178125|53.6|2025-49|0.91|16.8|7.8|2.29|72.0|76.5|23|29|
|2050|6499395|54.i|2050-74|0.48|13.9|9.2|2.08|75.8|81.1|10|12|
|2075|7323357|60.0|2075-99|0.24|12.7|10.3|2.06|78.5|84.3|5|7|
|2100|7734287|67.0|2100-24|0.12|12.1|11.0|2.06|80.7|8Y.0|3|4|
|212S<br>2150|8015877<br>8131561|70.6<br>72.5|2125-49|0.06|11.9|11.3|2.05|81.6|88.1|2|3|

### 2. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 6 2 1 ~~~~75 +<br>70-4<br>2 B f e~~~O-e4<br>] 1 ~~~~55-59 iss2<br>50-54<br>45-49<br>40-44<br>35.39<br>30-34<br>25-29<br>20-24<br>15-19<br>10.14<br>S2ES SXn ~~~ ~~5-9 R2<br>.. WfiMM.M. _ jo0-4 ________<br>10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text --> <!-- Start of picture text --> 10 8 6 4 2 0 2 4 6 810<br><!-- End of picture text -->

## 3. Distribution of population (percent): 1990 and 2025

<!-- Start of picture text --> H4IS C1 4C 'i': 11 1%)<br>hwassom Co 'U0)<br>"l°~~~~~~~~~~~~~~~~~~.,uoCl'. LOW-ID Ce M-tt<br>LOW<br>Loop-mlo ~ ci4 ~ ~ ~ ~ ~ ~ ~ 1<br><!-- End of picture text -->
# Using Post-Double Selection Lasso in Field Experiments<sup>∗</sup>

Jacobus Cilliers, _Georgetown University_ Nour Elashmawy, _Development Research Group, World Bank_ David McKenzie, _Development Research Group, World Bank_

**Keywords:** Treatment effect; Randomized Experiment; Post-Double Selection Lasso; Attrition; Statistical Power. **JEL classification codes** : C93; C21; O12.

> ∗We thank authors of the different papers we replicated for answering queries on their code; Christian Hansen, Kaspar Wuthrich, Carolina Lopez, Anja Sautmann, Greg Lane, Erin Kelley, and participants in the World Bank half-baked seminar for useful comments. Elashmawy was funded by the Robert S. McNamara Fellowships Program.
# **Figure 3. Oil demand trends**

<!-- Start of picture text --> A. Oil intensity of GDP and energy consumption B. Global stock of electric cars<br>Percent Index, 100=1965 Millions<br>Oil intensity of energy consumption 2.5 China<br>60 Oil intensity of GDP (RHS) 120 United States<br>2.0 Rest of world<br>50<br>100<br>1.5<br>40<br>80<br>30 1.0<br>20 60 0.5<br>10 40 0.0<br>1965 1975 1985 1995 2005 2015 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Sources: BP Statistical Review of World Energy, International Energy Agency Global EV Outlook, World Bank. A. Oil intensity of energy consumption measured as oil consumption in percent of total primary energy consumption. Oil intensity of real GDP measured as oil consumption relative to real GDP. Last observation is 2016. B. Electric car stock includes battery-electric vehicles and plug-in hybrid electric vehicles.

17
Unemployment, Migration and Wages in Turkey, 1962-85

# by

# Bent Hansen

# Content

page

|Introduction<br>........................................|... iii|
|---|---|
|1. LTVaemployment,<br>1962-85<br>...............................|...<br>1|
|Notes to Section 1<br>...................................<br>Tables 1.1-<br>1.9<br>......................................|.<br>21<br>22|
|2. Internal and External Migracion<br>..|32|
|Tables 2.1-<br>2.4..<br>.36||
|3. Nominal Wage Rates-<br>Areas of Knowledge and Ignorance<br>|.<br>39|
|Notes to Section 3 .54||
|Tables 3.1-<br>3.7.56||
|4. Phillips R.lation for Turkey .63||
|NL.ces to Section 4 .70||
|Tables 4.1-<br>4.3.|72|

The author would like to acknowledge Ms. Jariya Charoenwattana for her computational and other-assistance.
# _C. Thailand and Vietnam_

|||||**Thailand**|||||**Vietnam**|||
|---|---|---|---|---|---|---|---|---|---|---|---|
||||Occup|ation digit|al level|||Occup|ation digita|l level||
|||Low vs.<br>Very<br>low|Medium<br>vs. Low|High vs.<br>Medium|High vs.<br>Low|High vs.<br>Very<br>low|Low vs.<br>Very<br>low|Medium<br>vs. Low|High vs.<br>Medium|High vs.<br>Low|High vs.<br>Very<br>low|
|||(16)|(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|(25)|
|**Cognitive**|Thinking|0.26|0.21|0.08|0.46|**2.11*****|0.24|**0.25***|0.08|**0.65***|**2.59*****|
|||(0.18)|(0.13)|(0.09)|(0.25)|(0.40)|(0.14)|(0.12)|(0.08)|(0.25)|(0.28)|
||Communication|**0.60***|**-0.26***|0.35|-0.03|-0.15|**0.77*****|-0.25|0.38|0.24|-0.19|
|||(0.23)|(0.12)|(0.29)|(0.20)|(0.14)|(0.17)|(0.16)|(0.29)|(0.25)|(0.15)|
||Organization|0.10|-0.22|0.06|-0.19|-0.45|0.11|**-0.28***|-0.03|-0.42|-0.01|
|||(0.12)|(0.12)|(0.09)|(0.20)|(0.37)|(0.11)|(0.13)|(0.08)|(0.21)|(0.12)|
|**Socioemotional**|Emotions|-0.08|**0.51*****|-0.04|0.29|0.45|-0.14|**0.59*****|0.00|0.29|-0.01|
|||(0.11)|(0.12)|(0.10)|(0.21)|(0.38)|(0.08)|(0.13)|(0.09)|(0.22)|(0.09)|
||Relationships|-0.14|**-0.29***|-0.41|**-0.53***|-0.16|-0.09|-0.43|-0.49|**-0.84****|-0.09|
|||(0.11)|(0.14)|(0.33)|(0.24)|(0.12)|(0.07)|(0.23)|(0.31)|(0.27)|(0.07)|
||Personal growth|-0.13|0.03|0.27|0.37|-0.14|-0.16|0.14|0.20|**0.43***|-0.10|
|||(0.14)|(0.16)|(0.17)|(0.21)|(0.14)|(0.08)|(0.21)|(0.14)|(0.19)|(0.09)|
||Constant|0.22|1.48***|2.03***|1.17***|0.15|0.20*|1.57***|2.03***|1.17***|0.12|
|||(0.13)|(0.11)|(0.09)|(0.14)|(0.14)|(0.08)|(0.11)|(0.06)|(0.16)|(0.10)|
||N|79|76|48|54|51|79|76|48|54|51|
||R-sq|0.45|0.47|0.18|0.23|0.52|0.66|0.48|0.19|0.28|0.65|

## _Source_ : Authors’ calculations based SEAD data.

_Note_ : Independent variables are dummies taking the value of one if the skills count of a skills category of an occupation is above the meian and thus can be interpreted as percentage point increase (or decrease) towards the probability of being a given occupation digital level. Malaysia does not have one of the 127 occupations of the occupational skills profile (paramedical practitioners, ISCO code 224) according its 2017 labor force survey, so its total number of occupations is 126.

_*p < 0.1 **p < 0.05 ***p < 0.01_ 49
<!-- Start of picture text --> GtCO2eq<br>80<br>70 A. OECD Environmental Outlook Baseline<br>60<br>ROW<br>50<br>40<br>BRIC<br>30<br>20<br>OECD<br>10<br>0<br>1970 1980 1990 2000 2010 2020 2030 2<br><!-- End of picture text -->

**Fig. 2.** Anthropogenic green house emissions: OECD, BRICs and Rest of the World. (Source: http://www.oecd.org/environment/outlookto2030)

emissions do not play any role in the determination of the optimal tax, _tF_ , in (1)–(3). However, obviously, the size of the tax does affect final prices and energy efficiency.

The Ramsey and Pigou components dominate the determination of _tF_ , with the Congestion Feedback component playing a very minor role (note that the congestion externality is part of the Pigou component). The relative importance of the Ramsey and Pigou components in Table 3 is driven mostly by differences in producer prices, _qF_ (Table 6). The lower _qF_ the lower the Ramsey component— _e.g._ , compare Germany and the UK in Table 3, and note the difference in _qF_ in Table 6. Table 3 reports the implied excess burden of non-motor fuel taxation (1 + _MEBL_ ). (Remember that in this framework all other taxes are lumped into an aggregate labor tax.) These values are broadly in line with the lower-range of values used in the literature for the marginal cost of funds, between 1.1 and 1.25. Other things equal, the larger the _MEBL_ , the larger the optimal motor fuel tax.

Fig. 3 uses _box plots_<sup>13</sup> to summarize the results in Table 4, which shows the impact of changes in the values of key parameters in the results, across the different countries. The size of the response to the variation in a particular parameter depends on the values of all others, so these results inform on what matters most, given the configuration of parameters. We consider changes of 0.1 in the values of the labor and motor fuel elasticities, and changes of /c10 in the climate-change emissions damage.

There is large heterogeneity in the response across countries when altering three of the

> according to IEA.

> 13 A box plot is a histogram-like method of displaying data. The quartiles _Q_ 1 and _Q_ 3 delimit the box, while the statistical median is represented by a horizontal line in the box. The “whisker” are extended to the farthest points that are within 2<sup><u>3</u>timestheinterquartilerangeof(</sup><sup>_Q_3</sup><sup>_−Q_1).Then,everypointmore</sup> than<sup><u>3</u>theinterquartilerangefromtheendofabox,isrepresentedbyadot,anditisconsidered</sup> 2<sup>times</sup> an outlier.

9
Table 9: Propensity score matching full results

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||closest 5|neighbors|closest n|eighbor|pscor|e<.15|
||Language|Math|Language|Math|Language|Math|
|Years of exposure to FTS|0.08<br>(0.07)|0.2<br>(0.07)***|0.14<br>(0.09)*|0.2<br>(0.08)**|0.21<br>(0.08)**|0.29<br>(0.08)***|
|Pupil is female =1|1.15|0.32|1.18|0.45|1.16|0.21|
||(0.10)***|(0.11)***|(0.14)***|(0.14)***|(0.13)***|(0.12)*|
|Nr of people living in pupil’s hh.|-0.14|-0.09|-0.15|-0.1|-0.12|-0.07|
||(0.02)***|(0.03)***|(0.03)***|(0.03)***|(0.03)***|(0.03)**|
|Nr of rooms in pupil’s home|0.03|0|-0.01|-0.02|0.03|-0.03|
||(0.04)|(0.04)|(0.06)|(0.05)|(0.05)|(0.05)|
|Home equipm. modern appliances|0.57|0.53|0.57|0.53|0.51|0.46|
||(0.08)***|(0.08)***|(0.10)***|(0.10)***|(0.08)***|(0.09)***|
|Mother completed primary educ.|0.33<br>(0.14)**|0.41<br>(0.14)***|0.14<br>(0.19)|0.45<br>(0.22)**|0.4<br>(0.16)**|0.49<br>(0.16)***|
|Mother has some secondary educ.|0.45<br>(0.13)***|0.52<br>(0.12)***|0.66<br>(0.17)***|0.61<br>(0.17)***|0.46<br>(0.15)***|0.59<br>(0.15)***|
|Mother completed secondary educ.|0.33|0.22|-0.01|0.06|0.38|0.34|
||(0.21)|(0.18)|(0.28)|(0.26)|(0.26)|(0.24)|
|Mother completed tertiary educ.|0.58|0.65|0.42|0.52|0.43|0.55|
||(0.29)**|(0.30)**|(0.38)|(0.35)|(0.38)|(0.36)|
|Teacher exper. 1 year or less=1|-0.89|-1.21|-0.97|-1.44|-0.73|-0.81|
||(0.35)**|(0.33)***|(0.43)**|(0.46)***|(0.37)*|(0.31)***|
|Teacher experience in years|0.05|0.08|0.08|0.1|0.03|0.07|
||(0.01)***|(0.01)***|(0.01)***|(0.01)***|(0.01)***|(0.01)***|
|Class size|0.03|0.02|0|-0.02|0.01|-0.01|
||(0.01)**|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|This is an observation in 2002 =1|0.38|0.31|-0.03|0.24|0.33|0.21|
||(0.15)**|(0.17)*|(0.25)|(0.25)|(0.19)*|(0.18)|
|P-score|0.64|-0.04|0.55|-0.47|0.7|0.37|
||(0.35)*|(0.31)|(0.40)|(0.36)|(0.42)*|(0.36)|
|Constant|11.56|9.74|12.52|10.73|11.89|10.26|
||(0.40)***|(0.40)***|(0.56)***|(0.55)***|(0.46)***|(0.45)***|
|Observations|5910|5910|3431|3431|4313|4313|
|Number of Unique school identi...ers|83|83|49|49|63|63|
|Number of full time schools|28|28|28|28|23|23|
|Number of comparison schools|55|55|21|21|40|40|

Notes: This table reports the estimated program e¤ects under di¤erent sample restrictions. Columns 2 and 3 report the results when the sample is restricted to program schools and their 5 nearest neighbors in terms of p score. Columns 4 and 5 report the results when the sample is restricted to program schools and their closest neighbor in terms of p score. Finally, in Column 5 and 6, the sample is restricted to those schools with a propensity score higher than 0.15. All coe¢ cients and standard errors are bootstrapped using 300 replications. In all columns, observations that do not belong to the support of the p-score function are excluded. * signi...cant at 10 percent; ** signi...cant at 5 percent; *** signi...cant at 1 percent.

20
Finally, as with labor and social standards, the estimates are not due to a specific income group, but do apply to all. An outlier for the overall results is the negative and significant coefficient for the share of renewables in electricity generation (column 3). It does not fit the generally mediating role of standards for economic upgrading through GVCs and, therefore, does not affect the overall very positive conclusion we can draw with respect to standards.

**Table 11. The role of environmental standards for economic upgrading**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|_DVA_|_DVA_|_DVA_|_DVA_|_DVA_|_DVA_|
|**Backward links**|||||||
|_FVAX_|0.0555***<br>(0.0114)|0.118**<br>(0.0504)|0.0678***<br>(0.0104)|0.0638***<br>(0.0124)|0.146***<br>(0.0515)|0.0699***<br>(0.00985)|
|_FVADP_|0.258***|0.254***|0.254***|0.265***|0.252***|0.259***|
|_FVAX*Environm. ISOs_|(0.0276)<br>4.6e-05<br>(2.9e-05)|(0.0269)|(0.0269)|(0.0237)|(0.0352)|(0.0275)|
|_FVAX*UNEP conventions_||-0.0060<br>(0.0060)|||||
|_FVAX*Renewables_|||0.0005<br>(0.0017)||||
|_FVAX*Pesticides_||||-0.0008<br>(0.0019)|||
|_FVAX*Air pollution_|||||-0.0055**<br>(0.0027)||
|_FVAX*Water use_||||||-7.2e-05***|
|||||||(2.6e-05)|
|_Constant_|5.651***<br>(0.176)|5.937***<br>(0.188)|5.895***<br>(0.187)|5.875***<br>(0.178)|6.131***<br>(0.284)|5.870***<br>(0.189)|
|Observations|<br>7,037|<br>7,156|<br>7,156|<br>6,461|<br>3,356|7,037|
|R-squared|0.858|0.854|0.854|0.865|0.850|0.858|
|**Forward links**|||||||
|_DVAR_|0.187***<br>(0.0167)|0.0914<br>(0.0823)|0.238***<br>(0.0187)|0.264***<br>(0.0205)|0.280***<br>(0.0575)|0.220***<br>(0.0174)|
|_FVADP_|0.237***|0.234***|0.231***|0.245***|0.226***|0.238***|
|_DVAR*Environm. ISOs_|(0.0266)<br>0.0001***<br>(4.2e-05)|(0.0256)|(0.0258)|(0.0228)|(0.0304)|(0.0266)|
|_DVAR*UNEP conventions_||0.0157<br>(0.0098)|||||
|_DVAR*Renewables_|||-0.0049**<br>(0.0022)||||
|_DVAR*Pesticides_||||-0.0065***<br>(0.0020)|||
|_DVAR*Air pollution_|||||-0.0015<br>(0.0029)||
|_DVAR*Water use_||||||-9.5e-05<br>(8.7e-05)|
|_Constant_|5.314***|5.080***|5.255***|5.030***|5.170***|5.322***|
||(0.198)|(0.182)|(0.189)|(0.161)|(0.273)|(0.199)|
|Observations|7,045|7,164|7,164|6,464|3,356|7,045|
|R-squared|0.876|0.872|0.872|0.883|0.873|0.875|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. _FVAX_ and _DVAR_ lagged. All level variables in natural logarithms.

31
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