rafmacalaba/datause-encoder-data huggingface dataset:
|Share of dwellings<br>sprayed<br>D<br>D<br>(4)<br>(5)<br>IV<br>IV<br>0.263*<br>0.337***<br>(0.143)<br>(0.111)<br>0.218*<br>0.281**<br>(0.126)<br>(0.107)<br>0.293**<br>0.326***<br>(0.131)<br>(0.110)<br>-0.031<br>-0.000<br>(0.086)<br>(0.071)<br>0.064<br>0.083**<br>(0.046)<br>(0.038)<br>No<br>Yes<br>espective of their treatment<br>egression model augmented<br>clustered at village level are<br>hold size, tribe and religion,<br>e and share of women living<br>ouse sprayed in the previous<br>ng values are taking value 0<br>hare of dwellings sprayed is<br>5 months out of the village|
|---|
|<br>C<br>(3)<br>IV<br>0.350***<br>(0.122)<br>0.327***<br>(0.122)<br>0.368***<br>(0.127)<br>0.016<br>(0.084)<br>0.094**<br>(0.043)<br>Yes<br>ny bed nets, irr<br>using a linear r<br>Standard errors<br>pondent, house<br>acteristics (siz<br>to have their h<br>mption C, missi<br>rage, i.e. the s<br>in the previous|
|elling sprayed<br>B<br>(2)<br>IV<br>0.413***<br>(0.116)<br>0.345***<br>(0.113)<br>0.385***<br>(0.119)<br>0.012<br>(0.078)<br>0.094**<br>(0.040)<br>Yes<br>ets” refers to a<br>3) is estimated <br>d using 2SLS.<br>status of the res<br>and village char<br>sehold reported<br>ayed). In Assu<br>f the village ave<br>elling sprayed|
|Dw<br>A<br>(1)<br>IV<br>0.279**<br>(0.109)<br>0.242**<br>(0.112)<br>0.294**<br>(0.115)<br>-0.017<br>(0.077)<br>0.068*<br>(0.038)<br>Yes<br> household. “N<br> Columns (1)-(<br>d (5) is estimate<br>and marriage<br>zone dummies<br>whether the hou<br>ing value 1 (spr<br>computation o<br>to have their dw|
|Endogenous regressor:<br>Assumption:<br>_Household level_<br>1. Number of nets owned by household<br>2. Number of ITNs owned by household<br>3. Number of observed nets used the night before<br>4. Number of observed nets left unused the night before<br>_Individual level_<br>5. Reported net use<br>Controls<br>Note: *** p_<_0.01, ** p_<_0.05, * p_<_0.1. One observation per <br>status, “ITNs” includes only LLINs and properly treated ITNs. <br>with an endogenous binary variables (model3). Columns (4) an<br>reported in parentheses. Controls include gender, age, education<br>information about access to water, dwelling characteristics, sub-<br>in the village). In Assumption A missing values for the question<br>5 months are removed. In Assumption B, missing values are tak<br>(not sprayed). In Assumption D, missing values are kept for the<br>defned as the share of respondents in the village who reported<br>population.|

21
declined from 3.7 percent in 1992 to 3.2 percent in 1994. The actual general government budget deficit declined from 4.5 percent in 1993 to 3.9 percent in 1994. A number of industrial countries have announced medium-term deficitreduction objectives. Based on these, the OECD projects the average actual general government budget deficit in industrial countries to fall from 3.9 percent in 1994 to 1.9 percent in 2000.16 Sustained progress on these efforts at fiscal consolidation will be needed to keep government absorption of private savings within the amounts available without putting further upward pressure on real interest rates.

Most current projections, including the World Bank's current baseline projections,'<sup>7</sup> assume that the fiscal consolidation efforts now underway in industrial countries will be sustained, and will be sufficiently strong to help avoid a global crowding-out scenario that could otherwise result, with potentially serious consequences for world real interest rates and capital flows. But achieving the necessary fiscal adjustment will be a major and difficult challenge for industrial country governments, and the possibility of a faltering of these adjustment efforts constitutes an important risk in the global capital market outlook.

A major area of fiscal adjustment will be the reform of social security systems. The unsustainability of present social security policies is indicated by the fact that the present value of unfunded public pension liabilities in most major industrial countries ranges from 100 to 250 percent of GDP (Chart 14)."8 That means that the implicit social security debt is a multiple of an already large explicit public debt (in gross terms, the latter now averages over 70% of GDP in industrial countries, having jumped from about 40% only fifteen years ago).

> **16** OECD(1995a).

> 17 World Bank (1995b).

> **'8** Van der Noord and Herd (1993) and World Bank (1994a).

22
# TABLI OF CONTCTS

|Introduct|Page<br>ion . . . . . . . . . . . . . . . ................|
|---|---|
|I.|**Tanzania**CaseStudy.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>**.**<br>**.**<br>**.**<br>**.**<br>.<br>. .<br>.<br>.<br>.<br>**2**|
||1. Objectives . . . . . . . . . . . . . . . . . . .<br>.<br>.<br>.<br>. .<br>. 2<br>2. MethodologicalFramework<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>..<br>..<br>.<br>.<br>.<br>. 2<br>3. Using the Framework for Diagnostic Purposes . . .<br>.<br>.<br>. .<br>. 7|
||4. Identifying Specific Problems . . .<br>.<br>.<br>.<br>.<br>.<br>. .<br>.<br>.<br>. .<br>11|
||5.Developing Potential Solutions . . .<br>.<br>.<br>.<br>.<br>.<br>. .<br>.<br>.<br>.<br>.<br>13|
||6.Multi-Year<br>Prograuuing.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>17|
||7. Conclusions for Methodology.<br>.<br>.<br>.<br>.<br>.<br>. ..<br>.<br>. .<br>.<br>.<br>.<br>18|
|II.|Other<br>Countries<br>. .<br>.<br>.<br>.<br>. .<br>.<br>.<br>.<br>.<br>. .<br>.<br>.<br>.<br>.<br>.<br>.<br>. .<br>.<br>.<br>19|
||1.Objective<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.....<br>19|
||2. Data Collection and Sources.<br>.<br>.<br>. . . . . . . . . . . .<br>20|
||3. Quality of Data<br>21|
||**_A4_**<br>ConclusionsFromthe Nubers...<br>.<br>.<br>.<br>.<br>.<br>22|
||5.Scope forImproving<br>Data Quality . . . . . . .<br>.<br>. .<br>.<br>.<br>.<br>24|
|III.|OverallConclusios<br>.................<br>26|
|Figures:||
||1. Tax Avoidancet Vehicle ImportDuty.<br>.<br>.<br>.<br>.<br>.<br>.<br>. .<br>.<br>.<br>.<br>12|
|Attachme|nts:|
||1. Draft Consultation<br>Document.<br>.<br>.<br>. .<br>.<br>.<br>.<br>.<br>.<br>. .<br>.<br>.<br>.<br>27|
|Tabless||
||1. Overall<br>Financial<br>Performance<br>of the Transport<br>Sectors 1985/86.<br>. . . . . . . . . . . .<br>. . . . . . .<br>.<br>. 4<br>2. Overall Financial<br>Performance<br>of the Transport<br>Sectors 1986/87.<br>.<br>.<br>.<br>.<br>...<br>.<br>.<br>.<br>.<br>5<br>3. Road User Revenues and Road Expenditures.<br>.<br>.<br>.<br>. . . 8|
||4. Road Sector Financing Plan.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>..<br>.<br>16|
||5.Suimary of Fiscal FlowsToand From Goverment<br>.<br>.<br>23|

This paper was prepared as part of a project on Pricing, Cost Recovery and Efficient Resource Use in Transport. It was prepared by Ian G. Heggie, INUTD, and Michael QuLck, consultant.
# TABLE OF CONTENTS

|EXECUT|IVE SUMMARY ..........................................................|<br>i|
|---|---|---|
|I.|INTRODUCTION<br>..................................................|......<br>1|
|II.|TAX REGIMES OF THAILAND ANDCAPITAL-EXPORTING<sup>COUNTRIES....</sup><br>..|.......<br>7|
|III.|IMPACT OF TAXATION ON MULTINATIONALS'<sup>FINANCING AND INVESTMENT</sup><br>DECISIONS .....................................................|...<sup>22</sup>|
|IV.|POLICY OPTIONS FROM THE PERSPECTIVE OF THAILAND<sup>.................</sup>|<sup>... 46</sup>|
|V.|INTERNATIONAL STANDARDS FOR TAX POLICY ..........................|...<sup>63</sup>|
|APPEN|DIX: TAXATION OF MULTINATIONAL INVESTMENT:<sup>DERIVATION OF</sup><br>TECHNICAL RESULTS ........................................|...<sup>71</sup>|
|REFER|ENCES ...........................................................|..<sup>97</sup>|
**Table 5: Indicators/ data sets measuring data openness**

|**Indicators/Data sets**|**Author**|**% of elements focusing on**<br>**data access**|**Number of countries**<br>**covered(year)**|**MENA countries coverage**|
|---|---|---|---|---|
|1. Open Data Inventory (ODIN)<br>-2018/2019 and 2020|Open Data Watch|50% (5 out of 10)|178 (2018/2019)<br>187 (2020)|17 of 20 in 2018/2019 -<br>Algeria, Djibouti, Egypt, Arab<br>Rep., Iraq, Iran, Islamic Rep.,<br>Jordan, Kuwait, Lebanon,<br>Libya, Malta, Morocco,<br>Oman, Qatar, Saudi Arabia,<br>Syrian Arab Republic, Tunisia,<br>United Arab Emirates<br>All 20 MENA countries in<br>2020|
|2. Open Data Barometer (ODB)<br>– The leadership edition|World Wide Web<br>Foundation|Around 33.3 %|30 (2017)<br>Note: Scope of study was<br>reduced to 30 countries<br>in 2017 to include only<br>countries thatpublicly<br>committed to adopt<br>the International Open<br>Data Charter<br>Principles or the<br>equivalent G20 Anti-<br>Corruption Open Data<br>Principles.|1 of 20 – Saudi Arabia|
|3. Global Open Data Index<br>(GODI)|Open Knowledge<br>Foundation|About 50%|94 (2015)|3 of 20- Iran, Islamic Rep.,<br>Tunisia,Oman|

**Source: Author’s compilation**

17
Policy Research Working Paper 5057

# **Abstract**

Access to electricity is crucial for economic development and there is a growing body of literature on the impact of rural electrification on development. However, most studies have so far relied on cross-sectional surveys comparing households with and without electricity, which have well known causal attribution problems. This paper is one of the first studies to examine the welfare impacts of households’ rural electrification based on panel surveys conducted in 2002 and 2005 for some 1,100 households in rural Vietnam,. The findings indicate that grid electrification has been both extensive (connecting all surveyed communes by 2005) and intensive (connecting almost 80 percent of the surveyed households by 2005). Vietnam is unusual in that once electricity is locally available, both rich and poor households are equally likely to get the connection. The econometric estimations suggest that grid electrification has significant positive impacts on households’ cash income, expenditure, and educational outcomes. The benefits, however, reach a saturation point after prolonged exposure to electricity. Finally, this study recommends investigating the longterm benefits of rural electrification—not just for households, but for the rural economy as a whole.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the department to understand the role of infrastructure in development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at skhandker@worldbank.org.

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. 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 6b: Leverage across partner countries_

|Random effects estimation<br>Dependent variable: leverage<br>Explanatoryvariables|ratio||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|Intercept|-0.003|0.003|0|-0.002|0|-0.001|-0.001|0|0|0|
||(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|
|log(tangible assets)|-0.012***|-0.012***|-0.012***|-0.011***|-0.011***|-0.011***|-0.011***|-0.011***|-0.011***|-0.011***|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|Size of corporate|0.000**|0.000**|0.000*|0.000*|0.000**|0.000**|0.000**|0.000**|0.000**|0.000**|
|group|||||||||||
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|log(employees)|0.014***|0.014***|0.014***|0.014***|0.014***|0.014***|0.014***|0.014***|0.014***|0.014***|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|CY|0.008||||||||||
||(0.005)||||||||||
|RU||-0.015***|||||||||
|||(0.005)|||||||||
|NL|||0.026**<br>(0.012)||||||||
|GB||||0.021|||||||
|||||(0.016)|||||||
|DE|||||-0.008*||||||
||||||(0.005)||||||
|VG||||||0.013|||||
|||||||(0.012)|||||
|US|||||||0.008||||
||||||||(0.011)||||
|PL||||||||-0.013**|||
|||||||||(0.005)|||
|AT|||||||||0.005<br>(0.012)||
|LU||||||||||0.020**|
|||||||||||(0.009)|
|Obs|40254|40254|40254|40254|40254|40254|40254|40254|40254|40254|
|R|0.017|0.017|0.017|0.017|0.017|0.017|0.017|0.017|0.017|0.017|

27
the student body, and whether the school was day or boarding were not specLfLcally tested, being relatively non-manipulable variables.

BesLdes the models reported in Tables 3.3, 3.4 and 3.5 below, the following variables were tested using ML3, with no significant results: EQUAL, MQUAL, EXPER, MEXPER. It is interesting that school, rather than class level variables, ostensibly reporting **the same** type of informatLon, were found to be significant. For Lnstance, whether or not a particular class is taught by a trained and experienced teacher was not found to be significant, whereas the percentage of trained teachers overall in a school, was found to **be** a significant varLable. Except for the fLnal throe English models, the school level variance in both subjects is greator than the class **level** varianeo, and it may **be** that as was the **case** with the 1985 study, the class level variance is more intractable in the facea of **these** more readLly obtainable data and that classroom observations alone will provlde the clue to explaining class **level** differences in achievement.

A sex and Grade 7 interaction term was tested for Mathematics achievement. Whilst statistically significant, the explanatory power of the model was hardly improved and so further modelling with this variable was discontinued. A variable comprising the top three grades in the Grade 7 examlnation **was** composed for both subjects and found to be significant in the case of gnglish but not of Mathematics. The coefficient of this variable 'HIGH' for Engllsh was 1.47 (.05). Thli means that a stronger relationship exists botween high achievement in Grade 7 English and ZJC results than for 28
# **References**

Aguilar, Angel, and Terrie Walmsley, 2012. “GTAP 8 Database Documentation, Chapter 7, Regional Input‐Output Data,”

- Anderson, J. E. and E. van Wincoop 2003. “Gravity with Gravitas: A Solution to the Border Puzzle.” _American Economic Review_ , 93(1): 170–92.

- Arnold, Jens M. & Javorcik, Beata S. & Mattoo, Aaditya, 2011. "Does services liberalization benefit manufacturing firms?: Evidence from the Czech Republic," Journal of International Economics, Vol. 85, No. 1, pp. 136‐146, September.

- Arnold, Jens M. & Mattoo, Aaditya & Narciso, Gaia 2008. "Services Inputs and Firm Productivity in Sub‐ Saharan Africa: Evidence from Firm‐Level Data," Journal of African Economies, Vol. 17, No. 4, pages 578‐599.

- Arnold, Jens, Beata Javorcik, Molly Lipscomb & Aaditya Mattoo, 2010. "Services Reform and Manufacturing Performance: Evidence from India," CEPR Discussion Papers 8011, C.E.P.R. Discussion Papers.

- Baldwin, R., and D. Taglioni. 2006. “Gravity for Dummies and Dummies for Gravity Equations.” NBER Working Paper No. 12516, National Bureau of Economic Research, Cambridge, MA.

- Borchert, Ingo, Batshur Gootiiz, and Aaditya Mattoo. 2012a. “Policy Barriers to International Trade in Services: New Empirical Evidence,” Policy Research Working Paper Series No. 6109, World Bank, Washington, DC.

- ——. 2012b. “Guide to the Services Trade Restrictions Database.” Policy Research Working Paper Series No. 6108, World Bank, Washington DC.

- Feenstra, Robert C. 2010. _Offshoring in the Global Economy: Microeconomic Structure and Macroeconomic Implications_ . Cambridge, MA: MIT Press.

- Feenstra, R. C. (2004). Advanced International Trade: Theory and Evidence. Princeton, NJ: Princeton University Press.

- Francois, Joseph, and Bernard Hoekman. 2010. “Services Trade and Policy.” _Journal of Economic Literature_ 48 (3): 642–92 **.**

- Francois, J., and O. Pindyuk (2012). “Explanatory Note: Consolidated Data on International Trade in Services.” Unpublished.

- Francois, Joseph, Miriam Manchin, and Patrick Tomberger. 2013. “Services Linkages and the Value Added Content of Trade.” Working Paper 6432, World Bank, Washington, DC.

- Francois, Joseph, Olga Pindyuk, and Julie Woerz. 2009. “Trends in International Trade and FDI in Services.” IIDE Discussion Paper 200908‐02, IIDE, Rotterdam, Netherlands.

- Francois, Joseph, and Julia Woerz. 2008. "Producer Services, Manufacturing Linkages, and Trade." _Journal of Industry, Competition and Trade_ , 8 (3): 199–229.

- Freund, Caroline, and Martha Denise Pierola, 2015.  “Export Superstars.” _Review of Economics and Statistics_ , Vol. 97 No. 5 (December), 1023‐1032.

23
**Should the Food Insecurity Experience Scale Crowd Out Other Food Access Measures? Evidence from Nigeria**<sup>_∗_</sup>

By Jonathan Lain<sup>_†_</sup> , Sharad Tandon<sup>_‡_</sup> , and Tara Vishwanath<sup>_§_</sup> Keywords: Food Security; Measurement; Nigeria JEL Classification: D12; I31; I38; O10; O53

> _∗_ The views expressed here are those of the authors and may not be attributed to the World Bank.

> _†_ The World Bank, 1818 H St. NW, Washington, D.C. 20420, USA, jlain@worldbank.org.

> _‡_ The World Bank, 1818 H St. NW, Washington, D.C. 20420, USA, standon3@worldbank.org.

> _§_ The World Bank, 1818 H St. NW, Washington, D.C. 20420, USA, tvishwanath@worldbank.org.
the smaller countries, the levels of inequality in these countries are significantly higher than in the federal countries.

**Figure 4.2a: Regional disparity trends in unitary countries**

<!-- Start of picture text -->
1.8<br>1.6<br>1.4<br>1.2  _<br>*j0.8<br>.',i  '  ft'>,#  ."i4J.  -A<br>0.6<br>0.4  2  ....  ;;  i*<br>0.2  2.  . -.  .;<br>0<br>1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999<br><!-- End of picture text -->

<!-- Start of picture text --> Year<br>- Chile China China (new) Indonesia Philippines<br>.-.A- Romania -- Sri Lanka +Thailand -Uzbekistan Vietnam<br><!-- End of picture text --> _Romania,_ which has the lowest level of regional inequality experienced a rise in regional inequality during the period 1993 to 1996. Its inequality, measured by the weighted CV, is around a third more than that of the US. Another transition country, _Uzbekistan,_ has experienced a decline in its regional inequality during 1996 to 1999. Its weighted CV is around two and a half times more than that of the US and Canada. Uzbekistan is a highly centralized country with 14 regions whose leaders can be sacked by the country's President. The most important activity happens in a sparsely populated region, Navoi, where most gold and uranium is mined. As gold is one of its major exports, this could account for some of Uzbekistan's regional inequality.

23
<!-- Start of picture text --> 2£<br>=<br>on<br>3<br>e<br>ui<br>2<br>Ww<br>2010 2012 2014 2016 2018 2020<br>Year<br>——— Mother (6-year-old) ——— Mother (3-to-4-year-old)<br>Women without kids<br>Source: EU-LSF (2010-2019).<br><!-- End of picture text --> <!-- Start of picture text -->
-<br>aoS<br>Fi:<br>3<br>2 8<br>—&<br>ui<br>ozs<br>~Co<br>2010 2012 2014 2016 2018 2020<br>Year<br>Source: EU-LSF (2010-2019).<br><!-- End of picture text -->
Table 4 Baseline regressions

|Dependent Variables|SurvivalPi,<br>t|Sales<br>growthi, t|Output<br>demand<br>stablei t|Input<br>supply<br>stablei t|Liquidity<br>stablei, t|New<br>Productsi, t|Survivali, t|Sales<br>growthi, t|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|,<br>(3)|,<br>(4)|(5)|(6)|(7)|(8)|
|Non-smalli, t-1|0.089***<br>(0.012)|0.068***<br>(0.007)|0.075***<br>(0.011)|0.046***<br>(0.011)|0.073***<br>(0.010)|0.061***<br>(0.010)|0.068***<br>(0.013)|0.034***<br>(0.005)|
|Agei, t-1|0.014|0.009|-0.003|-0.006|0.015*|-0.016*|0.012|0.010**|
||(0.010)|(0.006)|(0.010)|(0.009)|(0.009)|(0.008)|(0.011)|(0.005)|
|Foreigni, t-1|0.033**|0.023**|0.025*|0.036**|0.056***|0.022|0.010|0.004|
||(0.017)|(0.010)|(0.015)|(0.016)|(0.017)|(0.017)|(0.019)|(0.008)|
|SOEsi, t-1|<br>0.038|<br>0.068***|<br>0.151***|<br>0.067*|<br>0.086**|<br>0.031|<br>-0.020|<br>0.022|
||(0.034)|(0.020)|(0.045)|(0.040)|(0.036)|(0.058)|(0.047)|(0.024)|
|Subsidiaryi, t-1|<br>0.035***|<br>0.014|<br>0.021|<br>0.011|<br>0.023*|<br>0.029*|<br>-0.004|<br>0.010|
||(0.013)|(0.009)|(0.013)|(0.015)|(0.013)|(0.017)|(0.014)|(0.006)|
|Log Experiencei, t-1|<br>0.020**|<br>-0.005|<br>-0.005|<br>0.012|<br>0.005|<br>-0.021**|<br>0.026***|<br>0.000|
||(0.009)|(0.005)|(0.009)|(0.008)|(0.007)|(0.008)|(0.009)|(0.003)|
|2<sup>nd</sup>Roundi, t|0.010<br>(0.031)|0.019<br>(0.016)|-0.001<br>(0.023)|-0.009<br>(0.030)|-0.158***<br>(0.022)|-0.040*<br>(0.024)|0.014<br>(0.041)|0.016<br>(0.012)|
|Log GDPperc, t-1|0.118***<br>(0.018)|0.090***<br>(0.007)|0.093***<br>(0.012)|0.111***<br>(0.012)|0.112***<br>(0.009)|-0.003<br>(0.013)|0.097***<br>(0.022)|0.043***<br>(0.007)|
|Policy stringencyc, t-1|-0.782***<br>(0.078)|-0.231***<br>(0.037)|-0.272***<br>(0.057)|-0.389***<br>(0.059)|-0.183***<br>(0.054)|0.056<br>(0.073)|-0.678***<br>(0.100)|-0.096***<br>(0.035)|
|Output demand stablei, t|||||||0.057***<br>(0.017)|0.233***<br>(0.010)|
|Input supply stablei, t|||||||<br>0.089***<br>(0.019)|<br>0.111***<br>(0.010)|
|Liquidity stablei, t|||||||0.208***<br>(0.014)|0.093***<br>(0.007)|
|New productsi, t|||||||<br>-0.007<br>(0.015)|<br>0.032***<br>(0.006)|
|Industry, Year FEs|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Observations|14,808|13,483|13,831|13,905|14,119|10,995|10,695|10,359|
|Adjusted R<sup>2</sup>|0.153|0.146|0.056|0.079|0.082|0.039|0.206|0.497|

Note: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticity-corrected standard errors clustered at the country-industry level in columns. The subscript i represents firm, j represents industry, c represents country, and t represents during the COVID-19 epidemic and t-1 represents a time before the COVID-19 outbreak. Year FEs labeled as Yes means that fixed effects of the year when implemented the lagged ES surveys (cover 2016-2019) are controlled for. The following tables are the same.

28
Figure 5: Comparison of estimates <!-- Start of picture text --> Daesh Reported Month<br>Production estimate with venting 95% Confidence Interval<br>Production estimate from Abu Sayyaf documents<br>September 2014 October 2014 November 2014 December 2014 Dec 2014 to Jan 2015<br>150<br>100<br>50<br>Daesh production ('000 bpd)<br>0<br><!-- End of picture text --> Note: Daesh production reported in Abu Sayyaf documents and our estimates with 95 percent confidence intervals for the corresponding periods. Units are in thousands of barrels per day.

Our findings, while lower than some accounts provided in the media, are consistent with some estimates for the early period of Daesh control that relied on extrapolation from pre-war field data [8] or captured documents [37]. In particular, administrative data captured from the Abu Sayyaf raid posits daily production ranging from 52,120 bpd to 55,560 bpd in the time frame from roughly June to October 2014. These numbers are well within the bounds of our 95-percent confidence intervals and actually very close to our point estimates (Figure 5).

14