rafmacalaba/datause-encoder-data huggingface dataset:
- Jaslowitzer, Patrick, William L. Megginson, and Marc S. Rapp. 2018. “State Ownership and Corporate Investment”, August 22

- Jiang, Jie, Jack Hou, Cangyu Wang, and HaiYue Liu. 2021. "COVID-19 Impact on Firm Investment— Evidence from Chinese Publicly Listed Firms," _Journal of Asian Economics_ 75(101320).

- Jurzyk, Emilia M. and Cian Ruane. 2021. “Resource Misallocation Among Listed Firms in China: The Evolving Role of State-Owned Enterprises,” IMF Working Papers, Working Paper No. WP/21/75 https://www.imf.org/en/Publications/WP/Issues/2021/03/12/Resource-Misallocation-AmongListed-Firms-in-China-The-Evolving-Role-of-State-Owned-50167

- Kopelman, Jason L. and Harvey S. Rosen. 2014. “Are Public Sector Jobs Recession-Proof? Were They Ever?”, Griswold Center for Economic Policy Studies, October.

- Lazzarini, Sergio G. and Musacchio, Aldo. 2018. “State Ownership Reinvented? Explaining Performance Differences between State‐Owned and Private Firms”, _Corporate Governance: An International Review_ , Vol. 26, Issue 4, pp. 255-272.

- Marrez. Helena. 2015. “The role of state-owned enterprises in Romania,” ECFIN Country Focus 12(1), European Commission, Brussels.

- OECD (Organisation for Economic Co-operation and Development). 2023. “OECD Review of the Corporate Governance of State-Owned Enterprises in Romania,” Corporate Governance, Paris: OECD Publishing.

- Olley, G. Steven, and Ariel Pakes. 1996. “The Dynamics of Productivity in the Telecommunications Equipment Industry. _Econometrica_ 64(6): 1263-1297.

- Sakha, Sahra, and Yosuke Jin. 2022. “Strengthening the business environment for productivity convergence,” in OECD Economic Surveys: Romania 2022, OECD Publishing, Paris, https://doi.org/10.1787/63318cf5-en.

- Szarzec, Katarzyna, Ákos Dombi, and Piotr Matuszak. 2021. “State-owned enterprises and economic growth: Evidence from the post-Lehman period,” _Economic Modelling_ 99(2021): 1-20. <u>https://www.sciencedirect.com/science/article/pii/S0264999321000730</u>

- Vitoria, Rafaela; Bressan, Aureliano and Iquiapaza, Robert. 2020. “Do State-owned Enterprises in Brazil Require a Risk Premium Factor?” _Brazilian Business Review_ , 17(5): 488-505.

- World Bank. 2018. “From Uneven Growth to Inclusive Development: Romania’s Path to Shared Prosperity.” Systematic Country Diagnostics. Washington, DC: World Bank.

- World Bank. 2020. “Markets and People: Romania Country Economic Memorandum.” International Development in Focus. Washington, DC: World Bank.

- World Bank. 2022. “Who’s the BOSs: Shedding New Light on Businesses of the State.” Equitable Growth, Finance, and Institutions Notes. Washington, D.C.: World Bank

32
Table 1: Summary statistics, estimation sample, 2007-2018

|Variables|mean|sd|min|max|mean|sd|min|max|
|---|---|---|---|---|---|---|---|---|
|||Lev|els|||Cha|nges||
|Weighted forecast error|-0.0576|0.7014|-14.3957|5.9409|0.1257|0.9272|-4.8760|14.4744|
|Weighted forecast growth|0.4503|1.4551|-2.8063|25.8987|-0.3424|1.7559|-24.6233|8.0253|
|log sales|14.9961|1.5749|7.9455|22.9203|-0.0742|0.4380|-6.4713|3.3672|
|log exports|12.6600|2.6719|0.0180|22.1646|-0.0878|1.3947|-14.8877|11.7114|
|log (1 + fixed tangible assets)|10.3196|3.4960|0.0000|20.2001|-0.5649|3.1132|-15.6905|15.1426|
|log (1+ intangible assets)|3.2776|4.4074|0.0000|19.5609|-0.0477|4.2399|-17.1895|16.5090|
|log employment|3.2396|1.3249|0.0000|10.1227|-0.0136|0.3178|-4.0431|3.1974|
|log value added|13.5306|1.5294|2.7091|20.9644|-0.0676|0.5215|-9.1936|4.4144|
|log value added per worker|10.2976|0.7017|1.0996|18.4347|-0.0540|0.4551|-9.0113|4.1573|
|log avg worker pay|9.6312|0.4456|6.8120|12.9764|0.0064|0.2032|-2.4518|3.3622|
|log monthly wage|7.0310|0.4080|6.1200|10.1300|0.0183|0.1920|-2.0650|3.1720|
|log hourly wage|1.8790|0.4110|0.9570|5.0310|0.0161|0.1920|-2.0900|3.1690|
|log monthly base wage|6.8110|0.3740|6.1200|9.9280|0.0139|0.1450|-1.5260|2.5270|
|log (1 + overtime pay)|0.7320|1.4420|0.0000|8.1080|0.0235|1.0080|-8.1080|7.4550|
|log (1+ other pay)|5.0070|1.2980|0.0000|9.9510|0.1200|1.1350|-8.3480|8.0050|
|log total hours|8.0160|1.4070|4.9130|14.6700|-0.0049|0.4130|-5.0790|5.8480|
|share with a degree|0.1470|0.1890|0.0000|1.0000|0.0164|0.0943|-1.0000|1.0000|
|N(obs.)||443|98|||22|199||

Notes: Table reports summary statistics on the firm-level data from the estimation sample for 2007-2018, both in levels and in changes. Levels refer to variables averaged over 3-year periods, changes refer to variation between 3-year periods.
<!-- Start of picture text --> Policy Research|<br>ovryAnalysis and Pollcy<br>WPS 925<br><!-- End of picture text -->

This paper - a product of the Poverty and Social Policy Division<sup>-</sup> Africa Technical Department - is part of a larger effort in the region to develop poverty-conscious<sup>macroeconomic frameworks in Sub-</sup> Saharan Africa. Copies of the paper are available<sup>free from the World Bank, 1818 H Street NW,</sup> Washington, DC 20433. Please contact Audrey Cox,<sup>room J2-264, extension 34778 (June 1992,88 pages).</sup> This paper reports on a general-equilibriumbased social policy model for Cote d'Ivoire - its structure, the data used in its implementation, and its application to the analysis of tax incidence and antipoverty programs.

The authors emphasize that special features of the Ivorian economy and tax system are central to any assessment of the distribution consequences of tax policies and other social policies in COte d'Ivoire. Because the bases of individual taxes tend to be narrow - the incidence effects by socioeconomic group are pronounced, while those by income range are milder. Urban employees are largely affected by income taxes (through withholding). Export food croppers are affected by the stabilization fund and export taxes. In addition, large interhousehold transfers in COte d'lvoire give incidence profiles that differ from conventional analysis. The reason: the taxes seemingly bome by one household group have second-round effects on other household groups through changed interhousehold transfers.

For the targeted antipoverty programs, the authors show that when general equilibrium effects are taken into account, it is impossible under a budget-neutral targeting program to completely eliminate poverty (as traditional analysis would suggest). At the same time, small transfers generally have a greater relative effect than larger targeting programs. This has several policy implications for the cost of the programs and the choice of groups that should benefit. As in the tax incidence analysis, the authors show that such domestic features as interhousehold transfers play an important role in determining the final outcome of the targeting program.

The model should be seen as an instrument that helps in developing capacitas for macroanalysis and in generally enhancing the economic debate. It should be transparent enough to enable policymakers to use it effi- ciently and at the same time answer some key questions of relevance to the Ivorian economy.

The Policy Research Working Paper Series disseminates the<sup>findings of work under way in the Bank. An objective of the series</sup> is to get these findings out quickly, even if presentations<sup>are less than fully polished. The findings, interpretations, and</sup> conclusions in these papers do not necessarily represent<sup>official Bank policy.</sup> Produced by the Policy Research Dissemination Center
which combine these aspects into a single indicator? The 1987 World Development Report (pp. 82-83) argues for the removal of all trade barriers based on links between growth and their own measure of trade policy which have been questioned by other studies (Helleiner, 1990, Singer, 1988). This measure combines orientation with the degree of intervention categorizing countries by "trade orientation". Categories range from "strongly outward oriented", characterized by very low trade controls, to "strongly inward oriented", in which the incentive structure favors production for the domestic market.

Table 7 reveals no svstematic relationship between what the Report calls outward orientation and relative price dispersion; inward oriented countries show _louwer_ median dispersion than outward oriented countries. Korea. one of the only countries to earn the Bank's label "strongly outward oriented", has a higher level of dispersion than Argentina and the Dominican Republic, both "strcngly inward oriented" countries. Perhaps unusually high price levels reflecting overvalued exchange rates in these two countries qualify them as inward oriented despite their relative lack of intervention; but Columbia, El Salvador and Pakistan, which are also classified as inward oriented, all have lower price dispersion than Korea and moderate price levels. Overall, "inward oriented" countries also have lower median price levels than "outward oriented" countries. Based on the WDR's orientation index alone, it seems that liberalization is not necessary to achieve outward orientation.

# 6 Comparisons of Relative Price Dispersion with other Inter-

# vention Measures

Since it is now clear that outward orientation is not svstematically correlated with intervention as measured by relative price dispersion, it is worth evaluating other measures of trade regime which 26
In all of the above cases, I map the respective GTAP sectors to Agriculture assuming that the larger share of value added accrues to it as compared to Industry. In the next section, I discuss how I use this data to quantify the gains from changes in distortions.

# **4 Quantitative Analysis**

I begin the quantitative analysis by calibrating the technology parameters θ _j_ and α _j_ . Equation 7 pins down the value of θ _j_ which is the share of sector _j_ ’s value added in the total value added. Using the sector value added data from the WDI database, I am able to estimate θ _j_ for multiple country-periods.7 As discussed before α _j_ maps to the capital share of income within a sector. I estimate α _j_ indirectly by estimating the labor share of income within a sector. To do this, I employ the GTAP data that reports both the value added as well as the labor income at the sector level. However, the GTAP data are available for only three years – 2004, 2007 and 2011. I estimate the labor shares for all the three years and then consider the average of the three estimates for our analysis. The estimated labor shares for the three years vary over a narrow range for a large number of countries. Hence, I take the average of the three years to serve as a reasonable benchmark and use a time-invariant set of sectoral labor shares for each country in the quantitative exercises that follow.

## **4.1 Estimating Distortions**

Once I have the estimates of φ _j_ and α _j_ , I employ equation 6 to arrive at the estimates of sectorspecific distortion τ _j_ . However, note that for any fixed country-period I have two independent equations and need to solve for three sector-specific distortions. But, since these distortions only determine the allocation of resources across sectors, I can normalize τ _j_ for any one sector and solve for distortions in the other two sectors relative to the distortion in the chosen sector. In this case, I set distortions in Agriculture at zero, i.e., τ _A_ = 0.8 Using this normalization, equation 6 can be rewritten in terms of τ _j_

> 7See data appendix for more details on the series used from the WDI database.

> 8This normalization does not alter the quantitative results obtained in the paper. A separate strategy that does not estimate distortions at sector level, but rather defines relative distortions as ratio of undistorted marginal products of any two sectors yields the same resource allocations. See appendix section on normalization for more details.

11
|CRISIS RESPONSE: EMPLOYMENT AND HUMAN CAPITAL SOCIAL SAF|Stefanie Koettl-Brodmann|
|---|---|
|CRISIS RESPONSE: EMPLOYMENT AND HUMAN CAPITAL SOCIAL SAF|Stefanie Koettl-Brodmann|
|CRISIS RESPONSE: EMPLOYMENT AND HUMAN CAPITAL SOCIAL SAF|Stefanie Koettl-Brodmann|
|CRISIS RESPONSE: EMPLOYMENT AND HUMAN CAPITAL SOCIAL SAF|Stefanie Koettl-Brodmann|
|Natural Gas EfficiencyProject|Anjum Ahmad|
|Natural Gas EfficiencyProject|Anjum Ahmad|
|Natural Gas EfficiencyProject|Anjum Ahmad|
|Natural Gas EfficiencyProject|Anjum Ahmad|
|Natural Gas EfficiencyProject|Anjum Ahmad|
|Natural Gas EfficiencyProject|Anjum Ahmad|
|Natural Gas EfficiencyProject|Anjum Ahmad|
|Natural Gas EfficiencyProject|Anjum Ahmad|
|Natural Gas EfficiencyProject|Anjum Ahmad|
|Natural Gas EfficiencyProject|Anjum Ahmad|
|Samoa Post Tsunami Reconstruction|James A. Reichert|
|Samoa Post Tsunami Reconstruction|James A. Reichert|
|Samoa Post Tsunami Reconstruction|James A. Reichert|
|Samoa Post Tsunami Reconstruction|James A. Reichert|
|Samoa Post Tsunami Reconstruction|James A. Reichert|
|Samoa Post Tsunami Reconstruction|James A. Reichert|
|Samoa Post Tsunami Reconstruction|James A. Reichert|
|Samoa Post Tsunami Reconstruction|James A. Reichert|
|Samoa Post Tsunami Reconstruction|James A. Reichert|
|Tonga Post Tsunami Reconstruction|Michael Bonte-Grapentin|
|Tonga Post Tsunami Reconstruction|Michael Bonte-Grapentin|
|Tonga Post Tsunami Reconstruction|Michael Bonte-Grapentin|
|Tonga Post Tsunami Reconstruction|Michael Bonte-Grapentin|
|Tonga Post Tsunami Reconstruction|Michael Bonte-Grapentin|
|Support to Reintegration of Ex-combatants and Special Needs Grou|Abderrahim Fraiji|
|Support to Reintegration of Ex-combatants and Special Needs Grou|Abderrahim Fraiji|
|Support to Reintegration of Ex-combatants and Special Needs Grou|Abderrahim Fraiji|
|Strengtheningand improvingtheperformance of thepublicprocur|eAndres Eduardo Mac Gaul|
|Good Financial Governance in Southern Africa - The Role of Parliam|Mitchell O'Brien|
|Good Financial Governance in Southern Africa - The Role of Parliam|Mitchell O'Brien|
|COMMUNITY DEVELOPMENT OF IMPROVED ACCESS TOQUALITY S|ENicolas Ahouissoussi|
|COMMUNITY DEVELOPMENT OF IMPROVED ACCESS TOQUALITY S|ENicolas Ahouissoussi|
|COMMUNITY DEVELOPMENT OF IMPROVED ACCESS TOQUALITY S|ENicolas Ahouissoussi|
|COMMUNITY DEVELOPMENT OF IMPROVED ACCESS TOQUALITY S|ENicolas Ahouissoussi|
|COMMUNITY DEVELOPMENT OF IMPROVED ACCESS TOQUALITY S|ENicolas Ahouissoussi|
|COMMUNITY DEVELOPMENT OF IMPROVED ACCESS TOQUALITY S|ENicolas Ahouissoussi|
|COMMUNITY DEVELOPMENT OF IMPROVED ACCESS TOQUALITY S|ENicolas Ahouissoussi|
|Second Sustainable and ParticipatoryEnergyManagement(PROGE|DAwa Seck|
|Second Sustainable and ParticipatoryEnergyManagement(PROGE|DAwa Seck|
|Second Sustainable and ParticipatoryEnergyManagement(PROGE|DAwa Seck|
|Second Sustainable and ParticipatoryEnergyManagement(PROGE|DAwa Seck|
|Second Sustainable and ParticipatoryEnergyManagement(PROGE|DAwa Seck|
|Second Sustainable and ParticipatoryEnergyManagement(PROGE|DAwa Seck|
**Aggregate Income Shocks and Infant Mortality in the Developing World**<sup>♣</sup>

Sarah Baird University of California at San Diego Jed Friedman World Bank Norbert Schady World Bank

> ♣ We thank Anne Case, Angus Deaton, Adriana Lleras-Muney, Christopher McKelvey, David Newhouse, Christina Paxson, Martin Ravallion, and seminar participants at the Center for Global Development, Princeton University, University of California at Berkeley, The Population Association Annual Meetings, and the World Bank for comments. All remaining errors are our own.
Policy Research Working Paper 9364

# **Abstract**

Recent research on productivity spillovers from affiliates of multinational corporations in developing and emerging economies finds that backward linkages from affiliates of foreign-owned firms to local suppliers constitute the main channel transmitting productivity spillovers. This finding has important policy implications, given that host economy governments often spend considerable resources on attracting multinational corporation investments and promoting their impact on technological development and economic growth. This paper conducts a new and comprehensive survey of recent empirical studies that focus on the drivers and impacts of backward linkages between multinational corporation affiliates and their local suppliers. The literature survey reveals that several characteristics of multinational corporation affiliates and domestic firms, host economy conditions, and various mediating factors influence the level of use of local suppliers, the nature and degree of technology dissemination, and the materialization of productivity spillovers among domestic firms. These findings are used to identify the main areas where policy making can be effective. The paper discusses various types of soft or light-handed industrial policies that host economy governments can design and implement to foster the extent of linkages between multinational corporations and local suppliers, facilitate technology dissemination, and enhance productivity spillovers among domestic firms.

This paper is a product of the Equitable Growth, Finance and Institutions Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at wdouw@ifc.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 6: Quarterly Drivers of Total Debt Outflows by Sector, 2004–2014

|||Panel A:|All Countri|es|||
|---|---|---|---|---|---|---|
||(1)<br>Total|(2)<br>Public|(3)<br>Banks|(4)<br>Corp.|(5)<br>Total +<br>Reserves|(6)<br>Public +<br>Reserves|
|log(VIX_t−_1)|-6.790<sup>_∗∗∗_</sup>|-0.398|-4.986<sup>_∗∗∗_</sup>|-1.407<sup>_∗∗∗_</sup>|-6.675<sup>_∗∗∗_</sup>|-0.282|
||(2.054)|(1.135)|(1.759)|(0.503)|(2.091)|(1.313)|
|GDP Growth_it−_1|0.130<sup>_∗∗∗_</sup><br>(0.0431)|0.0180<br>(0.0139)|0.0978<sup>_∗∗_</sup><br>(0.0359)|0.0145<br>(0.00982)|0.158<sup>_∗∗∗_</sup><br>(0.0432)|0.0460<sup>_∗∗_</sup><br>(0.0172)|
|Observations<br>|1408|1408|1408|1408|1408|1408|
|_R_<sup>2</sup>|0.047|0.002|0.043|0.015|0.051|0.006|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|
||Pa|nel B: Adv|anced Econ|omies|||
||(1)<br>Total|(2)<br>Public|(3)<br>Banks|(4)<br>Corp.|(5)<br>Total +<br>Reserves|(6)<br>Public +<br>Reserves|
|log(VIX_t−_1)|-11.61<sup>_∗∗∗_</sup>|0.0888|-9.121<sup>_∗∗_</sup>|-2.575<sup>_∗∗_</sup>|-10.66<sup>_∗∗_</sup>|1.040|
||(3.772)|(2.400)|(3.233)|(0.966)|(3.965)|(2.606)|
|GDP Growth_it−_1|0.339<sup>_∗∗_</sup>|0.0553|0.263<sup>_∗∗_</sup>|0.0204|0.337<sup>_∗∗_</sup>|0.0533|
||(0.116)|(0.0361)|(0.0969)|(0.0230)|(0.118)|(0.0401)|
|Observations|660|660|660|660|660|660|
|_R_<sup>2</sup>|0.082|0.004|0.087|0.025|0.074|0.004|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|
|||Pane|l C: EM||||
||(1)<br>Total|(2)<br>Public|(3)<br>Banks|(4)<br>Corp.|(5)<br>Total +<br>Reserves|(6)<br>Public +<br>Reserves|
|log(VIX_t−_1)|-2.223<sup>_∗∗∗_</sup><br>(0.588)|-0.813<br>(0.495)|-1.048<sup>_∗∗∗_</sup><br>(0.309)|-0.362<sup>_∗∗_</sup><br>(0.152)|-2.906<sup>_∗∗∗_</sup><br>(0.831)|-1.496<br>(0.958)|
|GDP Growth_it−_1|0.0387<br>(0.0195)|-0.00157<br>(0.00914)|0.0269<br>(0.0154)|0.0135<br>(0.00989)|0.0746<sup>_∗∗∗_</sup><br>(0.0234)|0.0343<sup>_∗∗_</sup><br>(0.0159)|
|Observations|704|704|704|704|704|704|
|_R_<sup>2</sup>|0.045|0.009|0.017|0.011|0.067|0.020|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|

Sample is from 2004q1-2014q4, countries as listed in Appendix A.4. Total debt is sum of Other Investment Debt and Portfolio Debt, outflow data is primarily from IMF BOP, as described in the text. Public outflows are defined as the sum of General Government and Central Bank outflows. Dependent variables are expressed as a percentage of GDP. VIX is the implied volatility of S&P 500 index options. GDP growth is calculated as a year-on-year percentage growth. Errors are clustered at the country level. ** p _<_ 0.05, *** p _<_ 0.01 36
# **Table 9**

**Drivers of Gross Capital Inflows: SUB‐SAHARAN AFRICA, 1980‐99 vs. 2000‐17** _Dependent Variable: GROSS Inflows, Total and By Type (% GDP) Sample: 26 countries, 1980‐2017 (annual) Estimation method: Instrumental Variables (IV)_

|Variables|Total<br>1980-99|Total<br>2000-17|FDI<br>1980-99|FDI<br>2000-17|PI<br>1980-99|PI<br>2000-17|OI<br>1980-99|OI<br>2000-17|
|---|---|---|---|---|---|---|---|---|
|_Domestic Factors_|||||||||
|GDP Growth|0.00532|-0.0625|-0.00457|0.0304|0.00236|-0.00184|0.00753|-0.0723|
||(0.0453)|(0.0794)|(0.0190)|(0.0586)|(0.00875)|(0.00816)|(0.0459)|(0.0769)|
|GDP per capita|0.439|-0.926|-0.227|0.530|0.117|-0.0207|0.549|-1.053|
|_(in logs)_|(1.934)|(1.295)|(0.812)|(1.109)|(0.374)|(0.156)|(1.959)|(1.246)|
|CPI Inflation|0.000765|-0.00468|0.000459|0.00155|-1.92e-06|-0.000234|0.000308|-0.00533|
||(0.000720)|(0.00394)|(0.000302)|(0.00254)|(0.000139)|(0.000289)|(0.000729)|(0.00443)|
|Primary Balance|-0.00360|-0.00153|-0.00123|-0.00305|0.000129|-0.000142|-0.00250|-0.000868|
|_(% of GDP)_|(0.00596)|(0.00566)|(0.00250)|(0.00239)|(0.00115)|(0.000338)|(0.00604)|(0.00545)|
|Exchange rate flexibility|-0.00672|-0.00666|-0.00174|0.0107|-0.000154|-0.00116|-0.00483|-0.00575|
||(0.0166)|(0.0284)|(0.00697)|(0.0325)|(0.00321)|(0.00431)|(0.0168)|(0.0299)|
|Investment Profile|-0.00316|0.0237|0.00999|-0.00414|0.00133|0.00137|-0.0145|0.0277|
||(0.0163)|(0.0254)|(0.00685)|(0.0220)|(0.00315)|(0.00237)|(0.0165)|(0.0309)|
|Trade Openness|-0.0220|0.223|-0.00299|-0.106|0.0279|0.00685|-0.0469|0.261|
|_(% of GDP, logs)_|(0.203)|(0.241)|(0.0852)|(0.214)|(0.0392)|(0.0278)|(0.205)|(0.254)|
|Financial Openness|0.0113|0.0933|-0.00599|-0.0752|0.00213|0.000200|0.0151|0.137|
|_(Chinn‐Ito Index)_|(0.0210)|(0.134)|(0.00883)|(0.0948)|(0.00406)|(0.0126)|(0.0213)|(0.137)|
|_External Factors_|||||||||
|Foreign Growth|0.0205|0.00924|0.00211|0.00888|-0.00569|-0.000607|0.0240|0.00673|
||(0.0275)|(0.0191)|(0.0116)|(0.0184)|(0.00532)|(0.00252)|(0.0279)|(0.0196)|
|VIX Index|-0.0608|-0.276|-0.000433|0.120|-0.00684|-0.0152|-0.0535|-0.296|
||(0.0755)|(0.328)|(0.0317)|(0.287)|(0.0146)|(0.0391)|(0.0765)|(0.327)|
|US Economic Policy|0.00135|0.00323|-0.000382|-0.00105|6.99e-05|0.000113|0.00167|0.00330|
|Uncertainty|(0.00370)|(0.00276)|(0.00155)|(0.00227)|(0.000715)|(0.000304)|(0.00375)|(0.00280)|
|Commodity Terms of|-0.114|0.0428|0.211|0.00239|-0.0649|0.00290|-0.261|0.0333|
|Trade (in logs)|(2.000)|(0.0584)|(0.840)|(0.0348)|(0.386)|(0.00380)|(2.026)|(0.0660)|
|<br>Oil Price|0.0664|-0.0218|0.00307|0.0111|0.00197|0.00109|0.0613|-0.0228|
|(in logs)|(0.172)|(0.123)|(0.0723)|(0.0872)|(0.0333)|(0.0102)|(0.174)|(0.138)|
|<br>Minerals and Metals|-0.166|0.0779|0.00858|-0.00533|0.0103|-0.000953|-0.185|0.0475|
|Prices (in logs)|(0.243)|(0.193)|(0.102)|(0.156)|(0.0469)|(0.0210)|(0.246)|(0.198)|
|Observations|76|370|76|381|76|380|76|371|
|Number of cnum|10|26|10|26|10|26|10|26|

_Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1_ 27