rafmacalaba/datause-encoder-data huggingface dataset:
- Das Varma . 2002. ``Standard Auctions with Identity-Dependent Externalities.'' _Rand Journal of Economics_ , 33.4, Winter, 689-704.

- Ettinger, D. 2002. ``Auctions and Shareholdings.'' Mimeo, CREST-LEI and CERASENPC, Paris.

- Haile, P. 2000. ``Partial Pooling at the Reserve Price in Auctions with Resale Opportunities.'' _Games and Economic Behavior_ , 33.2, 231-48.

- Horn Henrik and Petros C. Mavroidis. 2003. What Should Developing Countries Be Requesting From The DSU-Negotiation In The Doha Round? _Mimeo_ .

- Horn, Henrik, Petros C. Mavroidis and Håkan Nordström. 1999. Is The GATT Dispute .

- Settlement System Biased? _CEPR Discussion Paper Series_

- Jehiel, P. and B. Moldovanu. 1996. ``Strategic Nonparticipation.'' _Rand Journal of Economics_ , 27, 84-98.

- Jehiel, P. and B. Moldovanu. 2000. ``Auctions with Downstream Interaction Among Buyers.'' _Rand Journal of Economics_ , 31.4, Winter, 768-92.

- Jehiel, P. and B. Moldovanu. 2001. ``Efficient Design with Interdependent Valuations.'' _Econometrica_ , 69.5, 1237-59.

- Mavroidis, Petros C. 2000. Remedies In The WTO: Between A Rock And A Hard Place, 11 _European Journal Of International Law_ : 763-814.

- WTO. 2002. ``Dispute Settlement Body - Special Session - Negotiations on Improvements and Clarifications of the Dispute Settlement Understanding - Proposal by Mexico,'' November 4, catalogue record TN/DS/W/23.

32
Sunstein, C. R. 1999. “Informational regulation and informational standing: Akins and beyond.” _University of Pennsylvania Law Review_ . 147(3): 613–75.

Sunstein, C. R. 2013. _Simpler: The future of government_ . Simon and Schuster.

Toffler, A. 1970. Future shock. New York, NY: Random House Weil, D., Fung, A., Graham, M., and Fagotto, E. 2006. “The effectiveness of regulatory disclosure policies.” _Journal of Policy Analysis and Management_ 25(1):155-181.

Woodward, S. E. and R. E. Hall. 2012. “Diagnosing Consumer Confusion and Suboptimal Shopping Effort: Theory and Mortgage-Market Evidence.” _American Economic Review_ 102(7): 3249-76.

Zinman, J. 2015. “Household Debt: Facts, Puzzles, Theories, and Policies.” _Annual Review of Economics_ 7: 251-276.

26
# **3.1  Cross-Country Comparisons of Governance Using the WGI**

We use Figure 1 to emphasize the importance of taking these margins of error into account when making comparisons of governance across countries and over time. In the two panels of Figure 1, we order countries in ascending order according to their point estimates of governance in 2006 on the horizontal axis, and on the vertical axis we plot the estimate of governance and the associated 90% confidence intervals. These intervals indicate the range in which it is 90 percent likely that the true governance score falls.<sup>11</sup> We do this for two of the six governance indicators, Political Stability and Absence of Violence, and Control of Corruption. The size of these confidence intervals varies across countries, as different countries appear in different numbers of sources with different levels of precision. The resulting confidence intervals are substantial relative to the units in which governance is measured. From Figure 1 it should also be evident that many of the small differences in estimates of governance across countries are not likely to be statistically significant at reasonable confidence levels, since the associated 90 percent confidence intervals are likely to overlap. For many applications, instead of merely observing the point estimates, it is therefore more useful to focus on the _range_ of possible governance values for each country (as summarized in the 90% confidence intervals shown in Figure 1), recognizing that these likely ranges often overlap for countries that are being compared with each other.<sup>12</sup> This is not to say however that the aggregate indicators cannot be used to make cross-country comparisons. To the contrary, there are a great many pairwise country comparisons that do point to statistically significant, and likely also practically meaningful, differences across countries. Our 2006 Control of Corruption indicator for than those of our individual subjective measures. This underscores the fact that all efforts to measure governance involve margins of error, often non-trivial.

> 11 A x% confidence interval for governance can be obtained as the point estimate of governance plus or minus the standard error times the (100-x)/2<sup>th</sup> percentile of the standard normal distribution.  For example, the 90% confidence intervals we report throughout the paper are the point estimate plus or minus 1.64 times the standard error.

12 Of course, asking whether 90% confidence intervals overlap or not corresponds to a hypothesis test at a significance level that is more stringent than 10%. The assumptions underlying our statistical model imply that the standard error of the difference between two country scores is the square root of the sum of the squared standard errors of the two sources, which is always smaller than the sum of the two standard errors themselves. It is more convenient -- and more conservative -- for users to simply inspect confidence intervals and see whether they overlap.

15
# **References**

- Ambel, Alemayehu A, and Firew Bekele Woldeyes. 2024. _Does Unequal Tax Burden Contribute to Women-Owned Businesses Leaving the Tax Net?_ Technical report. The World Bank.

- Antinyan, Armenak, and Zareh Asatryan. 2024. “Nudging for Tax Compliance: a Meta-Analysis.” _The Economic Journal,_ ueae088.

- Asmare, Fissha, and Seid Yimam. 2020. “Gender and Tax Compliance: Firm Level Evidence from Ethiopia.”

- Awasthi, Rajul, Katie Pyle, Namita Aggarwal, and Parvina Rakhimova. 2023. “Gender-Based Discounts on Taxes Related to Property: Role in Encouraging Female Ownership — a Case Study of Indian States and Cities.” _Policy Research Working Papers._

- Benitez, Juan Carlos, Mario Mansour, Miguel Pecho, and Charles Vellutini. 2023. _Building Tax Capacity in Developing Countries._ Staff Discussion Note SDN/2023/006. International Monetary Fund, Washington, DC.

- Bergeron, Augustin, Gabriel Tourek, and Jonathan L. Weigel. 2024. “The State Capacity Ceiling on Tax Rates: Evidence From Randomized Tax Abatements in the DRC.” _Econometrica_ 92 (4): 1163–1193. eprint: https://onlinelibrary.wiley.com/doi/pdf /10.3982/ECTA19959. https: //onlinelibrary.wiley.com/doi/abs/10.3982/ECTA19959.

- Brockmeyer, Anne, Alejandro Estefan, Karina Ramírez Arras, and Juan Carlos Suárez Serrato. 2021. _Taxing Property in Developing Countries: Theory and Evidence from Mexico._ Working Paper, Working Paper Series 28637. National Bureau of Economic Research.

- Cabral, Ana Cinta G, Norman Gemmell, and Nazila Alinaghi. 2021. “Are survey-based self-employment income underreporting estimates biased? New evidence from matched register and survey data.” _International Tax and Public Finance_ 28:284–322.

- Cabral, Marika, and Caroline Hoxby. 2012. _The Hated Property Tax: Salience, Tax Rates, and Tax Revolts._ Working Paper, November. https://www.nber.org/papers/w18514.

- Castro, Lucio, and Carlos Scartascini. 2015. “Tax compliance and enforcement in the pampas evidence from a field experiment.” _Journal of Economic Behavior & Organization_ 116:65–82.

- Chancel, Lucas, Thomas Piketty, Emmanuel Saez, and Gabriel Zucman. 2022. _World Inequality Report 2022._ Harvard University Press. isbn: 9780674273566. http://www.jstor.org/stable/j. ctv3006zpt.

- Christensen, Darin, and Francisco Garfias. 2021. “The Politics of Property Taxation: Fiscal Infrastructure and Electoral Incentives in Brazil” [in en]. _The Journal of Politics_ 83, no. 4 (October): 1399–1416. https://www.journals.uchicago.edu/doi/10.1086/711902.

- Chung, Janne, and Viswanath Umashanker Trivedi. 2003. “The Effect of Friendly Persuasion and Gender on Tax Compliance Behavior.” _Journal of Business Ethics_ 47 (2): 133–145. http://www. jstor.org/stable/25075133.

16
a savings of $8 billion in the cost of meeting the poverty goal, more than double the aid provided to these countries in 1999.13

**_Table 11: Additional aid levels required to halve poverty_**

||**Current**<br>**ODAlevels**|**AdditionalODA**<br>**reachpove**|**requiredto**<br>**rtygoals**|
|---|---|---|---|
||**1999**<br>**($billions)**|**Improved**<br>**policies**|**Current**<br>**policies**|
|Officialdevelopmentassistance|57|54|62|
|GroupIcountries|40|||
|GroupIIcountries|18|54|62|
|GroupIIcountrieswithgood policies|15|39|39|
|GroupIIcountrieswithpoorpolicies|3|15|23|
|Totalaid(%ofhighincome OECDGNP)|0.25|0.37|0.45|

Both increased aid levels and improved policy performance will help to increase progress toward the goal of halving poverty by 2015. Under all scenarios, significant increases in aid to the group II countries are needed to meet the goal, but the countries with worse than average policies "need" a much greater percentage increase in aid to halve poverty. The projections shown in Table 11 suggest that between $39 and $54 billion in additional aid will be needed. This range represents the amount of aid required to enable reforming countries to reach the poverty goal. If the worse-performing countries fail to reform and are still given aid, the total bill could be as high as $62 billion.

There are serious limitations to these estimates, and they should be used cautiously. The historical data on income growth and poverty reduction are particularly weak in the poorest countries and in some countries where data are available a growing discrepancy between growth in income measured by the national accounts and mean income measured through household surveys has been observed (see Ravallion 2001). Furthermore, the projections depend critically on the assumption that changes in aid flows do not affect savings rates and ICORs. It is also assumed that the poor share equally in the benefits of growth. While this may be true on average, there are substantial differences among countries that could affect the aggregate level of aid requirements. Also, the rate of poverty alleviation in response to growth depends critically on the depth of poverty - the distribution of personal income in the vicinity of the poverty line.

These calculations assume that, with the exception of foreign aid, all other international exchanges continue as "business as usual." Specifically, we assume that private capital flows, already quite small in these countries, will not increase as a share of GDP. If instead private capital flows increased by one percentage point faster than GDP, the difference in the aid requirement would be negligible. If private capital flows in the weak-policy countries rose to the average of those with adequate policies, the additional aid required to meet the income poverty goal would be reduced by $0.5 billion.

**13** It is worth noting that this result was quite sensitive to the level of improvement we assumed in the policy framework. Under more optimistic assumptions about improvements in the macroeconomic, structural and public management frameworks, the additional aid saved would be $21 billion, rather than $8 billion.

19
# **Table 5a: Getting Electricity Indicators by Utility Ownership**

|Variable Name|Obs|Mean|**Public U**<br>Std.<br>Dev.|**tility**<br>Min|Max|Obs|**P**<br>Mean|**rivate Util**<br>Std.<br>Dev.|**ity**<br>Min|Max|**T-test**<br>Public vs.<br>Private|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Procedures Getting||||||||||||
|Electricity|133|5|2|3|11|50|5|1|3|10|0.4670|
|Time Getting||||||||||||
|Electricity|133|112|82|18|585|50|111|74|17|281|0.0527|
|Cost as % of GNI<br>pc Getting||||||50|1,185|1,622|0|6,526|1.7978|
|Electricity|133|2,524|5,161|6|36,697|||||||
|Cost in USD||32,13||||||||||
|GettingElectricity|133|1|28,683|876|148,938|50|33,331|55,124|0|386,549|-0.1917|

# **Table 5b: Number of Observations for Getting Electricity Indicators by Utility Ownership, Income Group and Region**

||||Incom<br>|e Group<br>|||||Region<br>||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Utility<br>Ownership|Entire<br>Sample|Low-<br>Incom<br>e|Lower<br>Middle<br>Income|Upper<br>Middle<br>Income|High<br>Income|East<br>Asia<br>&Pacific|Europe<br>&<br>Centra<br>l Asia|High<br>Income:<br>OECD|Latin<br>America<br>&Caribbean|Middle<br>East &<br>North<br>Africa|South<br>Asia|Sub-<br>Sahara<br>n<br>Africa|
|Public|133|28|37|32|36|20|15|21|17|15|7|38|
|% of total|73%|88%|69%|64%|77%|83%|63%|68%|53%|83%|88%|83%|
|Private|50|4|17|18|11|4|9|10|15|3|1|8|
|% of total|27%|13%|31%|36%|23%|17%|38%|32%|47%|17%|13%|17%|

# **Table 5c: Time and Cost of Getting Electricity controlling for income**

||Time Gett|ingElectricity|Costas % ofGN|Ipc GettingElectricity|
|---|---|---|---|---|
|Private utility|-0.702|1.798|-1338.251**|-1039.477**|
||(12.6)|(12.5)|(503.1)|(381.0)|
|Ln GNI per capita||-13.914**||-1662.882***|
|||(4.9)||(320.2)|
|Constant|111.602***|226.523***|2523.627***|16257.891***|
||(7.2)|(44.3)|(448.3)|(2,975.1)|
|Degrees of Freedom|181|180|181|180|
|R-squared|0.00<br>0.07|0.07|0.02|0.33|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||

36
overdrafts combined. The maturity information is available across three categories: less than one year, between one to five years, and above five years. Therefore, the maturity information for the ECCB is in the form of ‘triples’. As with the loans by purpose, this is available from 2000 onward.

# **D. Jamaica**

The data from Jamaica come from the Central Bank of Jamaica.<sup>33</sup> The sectoral distribution of commercial banks’ loans is available monthly from 1986. The December values from 2000 to 2014 are used to construct the annual time series. Commercial loans include loans to the following sectors: agriculture, mining, manufacturing, construction, transportation, utilities, distribution, tourism, entertainment, and other services. The consumer loan series for Jamaica is built by subtracting mortgage loans from personal loans. The mortgage loan series is sourced from the annual reports of the Central Bank of Jamaica.

# **E. Mexico**

Data for Mexico were sourced from the banking and securities superintendent of banks.<sup>34</sup> For the loans by purpose distribution of total gross loans, data are available monthly from 2011 and quarterly from 1998 in the statistical bulletins. December or fourth quarter values are used to construct an annual series. Commercial loans are constructed by using the commercial loans to firms and by excluding commercial loans to financial companies and to public entities. For the consumer loan series, the data are constructed by using the consumer series, and for mortgages, the loans for housing are used. This produces the sector series from 2004 to 2014. The maturity data for Mexico are available from 2009 onward. Mexico reports loans with maturity less than and more than one year; so, Mexico reports in ‘pairs’.

# **F. Paraguay**

The data from Paraguay come from the banking supervisory, which is part of the central bank.<sup>35</sup> The monthly statistical bulletins from 2010 to 2015 contain data on the distribution of loans by purpose. The end-of-December values are taken for the end-of-the-year values. Mortgage data only start in 2010; therefore, available sector data from 2008 and 2009 were not included in

> 33 Central Bank of Jamaica Statistics Portal. <u>http://www.boj.org.jm/statistics/econdata/stats_list.php?type=3.</u>

> 34 _Comisión Nacional Bancaria y de Valores_ . http://www.cnbv.gob.mx/Paginas/PortafolioDeInformacion.aspx.

> 35 _Superintendencia de Bancos_ . https://www.bcp.gov.py/supervision-financiera-i59.

41
# L I S T O F F I G U R E S

|Figure1|FDI Infow into Developing Countries<br>. . . . . . . . . .|. . .<br>2|
|---|---|---|
|Figure2|Assets and Sales by US Affliates,2008<br>. . . . . . . . . .|. . .<br>9|
|Figure3|Illustration of the Impact of Restricting FDI<br>. . . . . . .|. . .<br>14|
|Figure4|Randomness of Country Samples<br>. . . . . . . . . . . . .|. . .<br>18|
|Figure5|Quality of Institutions and Arbitration Regimes . . . . .|. . .<br>21|
|Figure6|Correcting for Country and Firm Selection Biases<br>. . .|. . .<br>25|
|Figure7|FDI and Statutory Openness<br>. . . . . . . . . . . . . . . .|. . .<br>31|
|Figure8|Reported and Unreported FDI Data . . . . . . . . . . . .|. . .<br>35|
|Figure9|Sorting of Firms by Productivity . . . . . . . . . . . . . .|. . .<br>53|

# L I S T O F TA B L E S

|Table1|Main Regressions: FDI Stock in2007-2008<br>. . . . . . . . . . .|29|
|---|---|---|
|Table2|Regressions: OECD Source Countries Excluded . . . . . . . .|30|
|Table3|Addressing the Zero Problem<br>. . . . . . . . . . . . . . . . . .|58|
|Table4|General Quality of Institutions (Graft)<br>. . . . . . . . . . . . .|59|
|Table5|General Quality of Institutions (Rule of Law)<br>. . . . . . . . .|60|
|Table6|General Quality of Institutions (Regulatory Quality)<br>. . . . .|61|
|Table7|General Quality of Institutions (Government Effectiveness)<br>.|62|
|Table8|General Quality of Institutions (Political Stability)<br>. . . . . .|63|
|Table9|Dummy for Resource Rich Countries . . . . . . . . . . . . . .|64|
|Table10|Regressions: FDI Stock in2006 . . . . . . . . . . . . . . . . . .|65|
|Table11|Regressions: FDI Stock in2007 . . . . . . . . . . . . . . . . . .|66|
|Table12|Regressions: FDI Stock in2008 . . . . . . . . . . . . . . . . . .|67|
|Table13|Description and Source of Variables . . . . . . . . . . . . . . .|68|
|Table14|Summary Statistics of Variables<br>. . . . . . . . . . . . . . . . .|70|
|Table15|Share of IAB Countries,2007-08<br>. . . . . . . . . . . . . . . . .|71|
|Table16|Lists of Countries<br>. . . . . . . . . . . . . . . . . . . . . . . . .|72|
|Table17|Correlation Among Explanatory Variables . . . . . . . . . . .|73|
|Table18|Correlation Among WGI Variables<br>. . . . . . . . . . . . . . .|74|
|Table19|Construction of the FDI Openness Variable<br>. . . . . . . . . .|75|
|Table20|Construction of the FDI Arbitration Variable<br>. . . . . . . . .|76|
|Table21|Construction of the FDI Procedures Variable<br>. . . . . . . . .|77|
|Table22|Composite Country Scores of Selected FDI Regulation . . . .|78|

iv
_Table 8. Propensity score estimation_

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||For. Acq =|For. Acq =|For. Acq =|For. Acq =|
|VARIABLES|1|1|1|1|
|log (Output)i,s,t−1|0.170***|0.165***|0.172***|0.163***|
||(0.0424)|(0.0423)|(0.0254)|(0.0253)|
|log (Capital)i,s,t−1|-0.023|-0.026|0.032|0.028|
||(0.0447)|(0.0442)|(0.0270)|(0.0267)|
|TFPR (CD)i,s,t−1|0.398||0.753***||
||(0.4634)||(0.2517)||
|TFPR (TL)i,s,t−1||0.197||0.402***|
|||(0.2017)||(0.1220)|
|Constant|-4.342***|-4.180***|-4.278***|-3.986***|
||(0.3035)|(0.2171)|(0.1688)|(0.1226)|
|Industry fixed effects|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|
|Observations|69,891|69,891|70,840|70,840|

Standard errors in parentheses. Lagged covariates correspond to the value one year before the acquisition in Columns (1) and (2) and all observations before the acquisition in Columns (3) and (4). The dependent variable is an indicator variable equals 1 if the domestic establishment was acquired by a foreign firm, 0, otherwise. TFPR (CD) denotes total factor productivity assuming a Cobb-Douglas production function, and TFPR (TL) represents total factor productivity assuming a translog production function. *** p<0.01, ** p<0.05, * p<0.1 After estimating propensity scores, we calculate weights to approximately preserve proportions between treatment and control groups and test the balancing property between treated and control groups. Table 9 summarizes these results by showing the difference in the means of key variables between treatment and control groups. The evidence suggests no statistically significant differences in output, capital, and TFP between treatment and control groups, regardless of whether we assume a Cobb-Douglas or translog production function.

_Table 9. Balance between treatment and control groups_

||(1)|(2)<br>Mean|(3)|(4)|(5)<br>t-test|
|---|---|---|---|---|---|
|VARIABLES|Treated|Control|Diff|t|Pr(|T|>|t|)|
|**N = 70,840**||||||
|log (Output)i,s,t−1|0.0207|0.0238|-0.0032|1.05|0.295|
|log (Capital)i,s,t−1|0.0203|0.0234|-0.0031|1.06|0.287|
|TFPR (CD)i,s,t−1|0.0009|0.001|-0.0001|0.91|0.361|
|TFPR (TL)i,s,t−1|0.0000|0.0000|-0.0000|0.14|0.886|
|Markupi,s,t−1|0.0037|0.0045|-0.0008|1.73|0.360|

Table shows the mean values of covariates for the reweighted sample. TFPR (CD) denotes total factor productivity assuming a Cobb-Douglas production function, and TFPR (TL) represents total factor productivity assuming a translog production function.

17
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