rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6. Price Rising Effect of NTMs Across all Countries** <u>(CIF Unit Values)</u>

|**Specification**|**NTM dummy**|**Log Number of NTMs**|**NTM dummy**|**Log Number of NTMs**|
|---|---|---|---|---|
|**Variables**|**[1]**|**[2]**|**[3]**|**[4]**|
|**NTM**<br>**SPS**<br>**TBT**|**0.101*****<br>**(0.008)**|**0.022*****<br>**(0.001)**|**0.194*****<br>**(0.011)**<br>**-0.015***<br>**(0.009)**|**0.023*****<br>**(0.002)**<br>**0.019*****<br>**(0.003)**|
|Log (GDP)|-0.082***<br>(0.003)|-0.087***<br>(0.003)|-0.083***<br>(0.003)|-0.087***<br>(0.003)|
|Log (Distance)|0.155***|0.161***|0.158***|0.161***|
||(0.004)|(0.004)|(0.004)|(0.004)|
|Log (H/L)|0.713***|0.704***|0.694***|0.705***|
||(0.013)|(0.013)|(0.013)|(0.013)|
|Log (T/L)|-0.160***<br>(0.004)|-0.151***<br>(0.004)|-0.156***<br>(0.004)|-0.151***<br>(0.004)|
|Log (K/L)|0.367***|0.375***|0.369***|0.375***|
||(0.005)|(0.005)|(0.005)|(0.005)|
|Contiguity|-0.071***|-0.065***|-0.068***|-0.065***|
||(0.007)|(0.007)|(0.007)|(0.007)|
|Common Official Language|0.120***|0.115***|0.119***|0.114***|
||(0.008)|(0.008)|(0.008)|(0.008)|
|Common Ethnical Language|-0.081***|-0.071***|-0.072***|-0.071***|
||(0.008)|(0.008)|(0.008)|(0.008)|
|Common Colonizer|0.007|0.006|0.006|0.007|
||(0.010)|(0.010)|(0.010)|(0.009)|
|Constant|3.088***|3.123***|3.104***|3.127***|
||(0.099)|(0.100)|(0.100)|(0.099)|
|Exporter FE|Yes|Yes|Yes|Yes|
|Product FE|Yes|Yes|Yes|Yes|
|Observations|1,922,527|1,922,527|1,922,527|1,922,527|
|R-squared|0.587|0.588|0.589|0.589|

Note: Robust standard errors adjusted for clustering at the product (6-digit HS codes) level are in parentheses. All regressions include exporter and product fixed effects. *** significant at the 1% level; ** significant at the 5% level; * significant at the 10% level.

Source: Authors’ computation Our results also indicate that SPS measures are positively and significantly related to an increase in border prices. The price increase arising from SPS measures is equivalent to an ad-valorem tariff of 21.4% across all countries and products in the dataset (column 3). By contrast, TBT measures are negatively related to border price with an estimated AVE of -1.5%. The coefficient for TBT is significant only at the 10% level. A negative AVE indicates a net trade-facilitating effect, which may be the result of positive externalities arising from the TBT outweighing its compliance costs (see Beghin et al, 2013). The signs and significance of the control variables are generally as expected.

In order to explore the impact of SPS and TBT measures on border prices across geographic regions, we use specification 3 and interact the SPS and TBT dummies variables with a dummy for each world region.

19
(2006), Canova and Gambetti (2009), Cogley et al. (2010), Koop and Korobilis (2013), Del Negro and Primiceri (2015), and Antonakakis et al. (2019).

The model is specified as: where _Yit_ is an _Nx_ 1 vector of endogenous variables, _Xit_ is a matrix of lagged dependent variables including an intercept, _B_ is a matrix of time-varying coefficients, and _εt_ is a time-varying independently and identically distributed error.

The VAR includes four variables: real output, consumer inflation, real policy rates–defined as the policy rate less expected inflation–and the real effective exchange rate. The models are estimated on quarterly data from 2000Q1 with two lags. The benefit of the TVP-VARs is that it applies less structure than the Taylor Rule and Laubach and Williams (2003) model to expected economic relationships.

In order to generate neutral real interest rate estimates from the TVP-VAR, five-year ahead iterated forecasts of the real interest rate are used as in Lubik et al. (2015); that is _rt_<sup>_∗_=</sup><sup>_rt_+20.13</sup>

# **6 Data**

Table 2 describes the data, sources and transformations used in this paper. All data are quarterly and collected as available, therefore, time spans depend on country-specific availability. The modeling strategy employed uses switches which turn on and off portions of the models for each country depending on data availability. Not all data is seasonally adjusted. When only non-seasonally adjusted data is available, the data is seasonally adjusted by Haver or using X-13ARIMA-SEATS.

In the Taylor rule model, inflation expectations are used instead of contemporaneous inflation as the variable of interest to the central bank. This approach limits the country choice to only those that have consensus forecasts available and from 2005 onwards. Since consensus forecast reports fixed event forecasts every month that do not account for the time dimension of forecasts appropriately, the fixed horizon transformation in Siklos (2013) and Bordo and Siklos (2017) is used. For each month the forecasts are available, inflation is transformed using: where FH is the fixed horizon forecast which is a linear combination of the fixed event forecast at time t (e.g. 2020) and t+1 (e.g. 2021) for month _m_ . This transformation ensures that the central bank is always targeting forecasts of annual inflation one-year ahead.

The VAR model uses output, inflation, interest rates, and the exchange rate. Output and the exchange rate are made stationary using a first difference log transformation. Inflation is the log annual change in consumer prices. The real interest rate is the ex ante real interest rate from the Laubach-Williams model.

> 13See Marcellino et al. (2006) for a discussion of directed versus iterated forecasts.

11
explain the difference.

Table 5: Vulnerability by gender of household head, 2011

||Headcount|V(prob)|E(Pov Gap)|E(Squared Gap)|Calvo-Dercon|
|---|---|---|---|---|---|
|Rural Households||||||
|Male-head|0.307|0.424|0.092|0.057|0.159|
|(Standard error)||(0.006)|(0.002)|(0.001)|(0.003)|
|Female-head|0.274|0.372|0.084|0.054|0.147|
|(Standard error)||(0.011)|(0.003)|(0.002)|(0.005)|
|Urban Households||||||
|Male-head|0.243|0.204|0.038|0.020|0.064|
|(Standard error)||(0.004)|(0.001)|(0.001)|(0.002)|
|Female-head|0.282|0.225|0.039|0.020|0.067|
|(Standard error)||(0.006)|(0.001)|(0.001)|(0.002)|

Notes: Survey-weighted proportion of population that have the characteristics. Other notes as in table 4 above.

Unreliable rainfall has historically underpinned much of the discussion on vulnerability in Ethiopia given the predominance of livelihoods that are dependent on rainfed production systems. This characterization of vulnerability has resulted in a widespread understanding of a geographic footprint of vulnerability, with more drought prone places characterized as more vulnerable. Safety net interventions have been targeted in these areas. We compared the level of vulnerability across geographic types (according to altitude and crop reliability). We find that vulnerability still has a strong geographic footprint in Ethiopia. The moisture reliable lowlands are the most vulnerable places in Ethiopia in 2011, followed by the Enset-growing lowlands and the drought-prone highlands. It may at first seem surprising that the moisture reliable lowlands are the most vulnerable places in Ethiopia, given that by their definition moisture is indeed more reliable in these areas. However, poverty, although falling, is still very high in the moisture reliable lowlands. More than half, 59%, of residents were poor in 2005. This fell substantially to 45% in 2011, though was still 13 percentage points higher than the next geographic area. Vulnerability is high in these lowlands not because residents are more subject to climate shocks that will drive them into poverty, but because residents are already poor. Lower levels of education and asset ownership result in our model predicting higher vulnerability in these areas.

However, even though vulnerability may have a geographic footprint in Ethiopia, much vulnerability is not geographically determined, but instead determined by other factors such as individual access to assets, or lifecycle events. This causes individuals everywhere, in every woreda of Ethiopia, to be vulnerable. We compared the proportion of vulnerable households in PSNP and non-PSNP woredas. Our results show that the PSNP is targeted to many of the most vulnerable areas: in particular, the vulnerable enset lowlands and to the drought prone highlands. However they also point to a need for safety nets in the moisture reliable lowlands, and at present very few woredas in these areas are included in the PSNP. Indeed, a safety net program that is targeted to 17
Figure 10. Evolution of Outcomes by Distance to the Refugee Camps – Economic Outcomes <!-- Start of picture text --> Night Light Density [0 to 63] Night Light Density (Sin*)<br>2.5 1.5<br>2<br>1<br>1.5<br>1<br>.5<br>.5<br>0 0<br>year year<br>Close to Refugee Camp Far from Refugee Camp Close to Refugee Camp Far from Refugee Camp<br>Deforestation (Ha/Km2) Deforestation (Sin*)<br>.008 .008<br>.006 .006<br>.004 .004<br>.002 .002<br>0 0<br>year year<br>Close to Refugee Camp Far from Refugee Camp Close to Refugee Camp Far from Refugee Camp<br>2001 2002 2004 2006 2008 2010 2012 2014 2016 2018 20202021 2001 2002 2004 2006 2008 2010 2012 2014 2016 2018 20202021<br>1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2021 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2021<br><!-- End of picture text --> _Notes:_ The figure illustrates the annual changes in night light density and deforestation outcomes, categorized into two groups: grids near and far from the refugee camps.
_Mathematics_ (Panel A), improvements are observed across the whole distribution of grades. Specifically, we observe both reductions in the proportion of students in the lowest grade category (‘Before beginning’) and improvements in the proportion in the ‘In process’ and ‘Satisfactory’ categories. As before, results are driven by the _regional_ sample. Point estimates obtained for the ‘Satisfactory’ category (ITT coefficients of 1 p.p. in the full sample and 2.3 p.p. in the regional sample) represent improvement by 17% and 38%, respectively. Similar results are observed for _Reading Comprehension_ (Panel B) but in this case, point estimates are smaller and, in some cases, marginally insignificant.

Overall, our estimates show strong evidence of the substantial impact of the intervention in the _regional_ sample, but not in the _metropolitan_ sample. It is difficult to single out a specific reason that might drive these results. One possible explanation is that “ _¡Expande tu Mente!_ ” is more likely to succeed among students with low-achievement—as evidence from other countries has also shown. At the same time, schools in the _metropolitan_ sample are larger and better equipped. It is likely that that the GYM intervention might have been more salient in a regional context, where teachers have fewer teaching tools at their disposal. Besides, the fact that the average classroom size is lower in the _regional_ sample might have contributed to the success of the GYM sessions—everything else equal, a session that requires all students to read an article is more likely to be effectively delivered in a smaller classroom.

# _<u>Robustness checks and heterogeneous estimates</u>_

There might be concerns that remote schools in the treatment group were more likely not to receive the ‘GYM package’, thereby potentially generating sample selectivity. To deal with this, we report results excluding remote schools from the control group. We do this by excluding schools from the control group located in areas the courier was not able to reach. These results largely resemble those obtained from our main sample (see **Table A3** in the Web Appendix). Also, our core analysis results remain robust to controlling for school characteristics that are different between schools assigned to treatment and control.<sup>20</sup> 20 Though the stratified randomization strategy was designed to minimize systematic imbalances between the treated and control samples, this possibility could not be ruled out. A key balancing test would have been to assess whether school attainment between treatment and control schools differed before treatment. Unfortunately, no school assessment data were available before 2015. Instead, we carried out a balancing test using administrative schoollevel data obtained from the 2014 Annual School Census. Treatment and control schools do 16
Table A.8: Robustness checks, alternative definition of undocumented migration. Estimating the effect of migrants’ legal status on hourly wages after return, unconditional on return migration

||(1)<br>Migrate|(2)<br>Illegal|(3)<br>Return|(4)<br>Log of<br>hourly wage|
|---|---|---|---|---|
|Age|0.012***|-0.091***|-0.018***|0.000|
||(0.001)|(0.033)|(0.007)|(0.005)|
|Age squared|-0.000***|0.001**|0.000***|0.000*|
||(0.000)|(0.000)|(0.000)|(0.000)|
|No education|0.021***|0.500***|-0.035*|-0.447***|
||(0.004)|(0.115)|(0.021)|(0.026)|
|Primary or preparatory|0.031***|0.423***|0.038*|-0.420***|
||(0.004)|(0.125)|(0.022)|(0.025)|
|Secondary|0.017***|0.333***|0.027|-0.287***|
||(0.003)|(0.102)|(0.018)|(0.020)|
|Rural|0.013***|0.137*|-0.006|-0.030*|
||(0.003)|(0.075)|(0.014)|(0.016)|
|Oil prices|0.013***<br>(0.000)||||
|Diplomatic exchange||-0.200**<br>(0.099)|||
|Confict|||0.059***<br>(0.014)||
|Illegal status||||-0.166**<br>(0.081)|
|Legal status||||0.135***<br>(0.052)|
|Observations|14,879|1,960|2,071|8,054|
|Parental controls|Yes|Yes|Yes|Yes|
|Decades of migration dummies|Yes|Yes|Yes|No|
|Labor market controls in Egypt|No|No|No|Yes|
|Predicted log wage: Non-mig.=1.|515; Illegal mig|.=1.384; Legal|mig.=1.727||

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. We use an alternative definition of undocumented migration. We define undocumented migrants as migrants who entered the destination country without having a visa or official document, for countries that required an entry visa, and as those who did not have a work contract for their employment abroad, for countries that had a visa waiver for Egyptians. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use the diplomatic exchange, which is the diplomatic representation of each country of destination of Egyptian migrants in Egypt at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange and is equal one if there is evidence of diplomatic exchange at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, unconditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row.

55
25 Communities and Local Government, 2008. _Building on Strong Foundation. A Framework for Local Authority Asset Management_ . - www.communities.gov.uk ESCAP, 1995. Economic and Social Commission for Asia and the Pacific (ESCAP), _Municipal Land Management in Asia: A Comparative Study._ Bangkok.

IPSAS. 2009. International Public Sector Accounting Standards. http://en.wikipedia.org/wiki/International_Public_Sector_Accounting_Standards#CoE_.28Counci l_of_Europe.29 Ito, Takatoshi and Frederic S. Mishkin. 2006. “Two Decades of Japanese Monetary Policy and the Deflation Problem” in Takatoshi Ito and Andrew Rose, eds. _Monetary Policy with Very Low Inflation in the Pacific Rim._ National Bureau of Economic Research and University of Chicago Press, pp. 131-193.

Ito, Takatoshi. 2010. China’s Property Bubble. Vox: Research-Based Policy Analysis. www.voxeu.org/index.php?q=node/4885 Kaganova, Olga. 2010. “Public Land and Property Asset Management in Warsaw: Strategic Opportunities.” Report Prepared for World Bank and City of Warsaw.

Kaganova, Olga, Abdirasul Akmatov, and Charles Undeland. 2008. “Introducing More Transparent and Efficient Land Management in Post-Socialist Cities: Lessons from Kyrgyzstan.” _International Journal of Strategic Property Management,_ V12, 161–181 _._ Kaganova, Olga. 2008. “Integrating Public Property in the Realm of Fiscal Transparency and Anti-Corruption Efforts” in _Finding the Money: Public Accountability and Service Efficiency through Fiscal Transparency,_ ed. Gabor Peteri. Open Society Institute (OSI), Budapest. http://cps.ceu.hu/jointpubl_findingmoney.php Kaganova Olga and James McKellar. 2006. _Managing Government Property Assets: International Experiences._ Washington DC: Urban Institute Press.

Kaganova, Olga, Bijan Azad, and Mithqal Sartawi. 2003. _Land and Real Estate in Kuwait: Market Analysis and Policy Implications._ World Bank-Urban Institute.

Korean Asset Management Corporation (KAMCO). 2006. _Principles of State Land Management, Second Edition_ . Seoul.

Kuwait. 2008. Law 7/2008, “Regulating Build, Operate & Transfer (BOT), Amending Provisions of Decree No. 105/1980 on the Regulation of Public Domain.” July.

Liu, Lili and Michael Waibel. 2008. "Subnational Borrowing, Insolvency, and Regulations." Chapter 6 in Anwar Shah ed. _Macro Federalism and Local Finance_ . Washington, DC: World Bank.

Liu, Lili and Michael Waibel. 2009. “Subnational Insolvency and Governance: Cross-Country Experiences and Lessons” in Ehtisham Ahmad and Giorgio Brosio, eds. _Does Decentralization Enhance Service Delivery and Poverty Reduction?_ Cheltenham, U.K.: Edward Elgar, pp. 333–76.

McKellar, James. 2006. “The Management Framework for Real Property—Government of Canada” in Olga Kaganova and James McKellar, eds. _Managing Government Property Assets: International Experiences._ Washington DC: The Urban Institute Press.
||**Table 7. Tes**|**ts of inflatio**|**n inequality**||
|---|---|---|---|---|
|**Country**|**Period**|**Constant**|**Percentile**|**R2**|
|**Brazil**|**1988-1996**|724.27*<br>(0.34)|0.07*<br>(0.01)|0.61|
|**Colombia**|**1997-2003**|10.34*<br>(0.04)|0.01*<br>(0.001)|0.48|
|**Mexico**|**1984-1989**|77.42*<br>(0.04)|0.01*<br>(0.001)|0.47|
||**1989-1994**|15.66*<br>(0.05)|0.02*<br>(0.001)|0.76|
||**1994-1996**|35.67*|-0.03*|0.79|
|||(0.08)|(0.001)||
||**1996-2002**|11.94*|0.01*|0.85|
|||(0.02)|(0.0003)||
|**Peru**|**1995-1999**|7.66*<br>(0.02)|0.01*<br>(0.0003)|0.83|
||**1999-2001**|3.41*|0.02*|0.84|
|||(0.04)|(0.001)||
||**2001-2003**|1.18*|0.01*|0.86|
|||(0.01)|(0.0003)||

Note: The table reports regression results with the inflation rate by percentile as dependent variable and the corresponding percentile and a constant as explanatory variables. Standard error in parethenses. (*) Significant at the 5%.

23
Tunisia: Jouini, Nizar, Nora Lustig, Ahmed Moummi and Abebe Shimeles. 2015. “CEQ Master Workbook: Tunisia. Version: October 1, 2015,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University and African Development Bank).

Uganda: Jellema, Jon, Astrid Haas, Nora Lustig and Sebastian Wolf. 2016. “CEQ Master Workbook: Uganda. Version: July 28, 2016,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University and International Growth Center).

Uruguay: Bucheli, Marisa, Nora Lustig, Maximo Rossi and Florencia Amabile. 2014. “CEQ Master Workbook: Uruguay. Version: August 18, 2014,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University).

36
- Hart, J.E., Yean, C.Y., Ivers, L.C., Behforouz, H.L., Caldas, A., Drobac, P.C. & Shin, S.S., 2010. Effect of Directly Observed Therapy for Highly Active Antiretroviral Therapy on Virologic, Immunologic, and Adherence Outcomes: A Meta-Analysis and Systematic Review. _Journal of Acquires Immune Deficiency Syndromes_ , 54(2): 167-179.

- Havlir D.V. & Lange J.M., 1998. New antiretrovirals and new combinations. _AIDS_ , 12(Supplement A): S165–S174.

- Kaptchuk, T., 2001. The double-blind, randomized, placebo-controlled trial: Gold standard or gold calf? _Journal of Clinical Epidemiology_ , 54(6): 541-549.

- Kunutsor, S., Walley, J., Katabira, E., Muchuro, S., Balidawa, H., Namagala, E. & Ikoona, E., 2010. Clinic Attendance for Medication Refills and Medication Adherence amongst an Antiretroviral Treatment Cohort in Uganda: A Prospective Study. _AIDS Research and Treatment_ , 2010: 1-8.

- Lange J., Perriens J., Kuritzkes D. & Zewdie D., 2004. What policymakers should know about drug resistance and adherence in the context of scaling-up treatment of HIV infection. _AIDS_ , 18(Supplement 3): S69-S74.

- Liechty, C.A. & Bangsberg, D.R., 2003. Doubts about DOT: antiretroviral therapy for resource-poor countries. _AIDS_ , 17: 1383-1387.

- MacLehose, R.R., Reeves, B.C., Harvey, I.M., Sheldon, T.A., Russell, I.T. & Black, A.M.S., 2001. A systematic review of comparisons of effect sizes derived from randomised and non-randomised studies. _Health Technology Assessment_ , 4(34): 1-153.

- Mannheimer, S.B., Mukherjee, R., Hirschhorn, L.R., Dougherty, J., Celano, S.A., Ciccarone, D., Graham, K.K., Mantell, J.E., Mundy, L.M., Eldred, L., Botsko, M. & Finkelstein, R., 2006. The CASE adherence index: A novel method for measuring adherence to antiretroviral therapy. _AIDS Care_ , 18:7, 853-861.

- Motsoaledi, A., 2011. _Health Budget Vote Policy speech by Minister of Health_ , National Assembly, 31 May, Cape Town.

- Nachega, J.B., Mills, E.J. & Schechter, M., 2011. Antiretroviral therapy adherence and retention in care in middle-income and low-income countries: current status of knowledge and research priorities. _Current Opinion in HIV & AIDS_ , 5(1): 70-77.

14
substantial immobility in the ranking of units within groups, in line with findings on persistence in inequality in Canova and Marcet (1995). To quote him, “As a consequence of the persistence of the initial income characteristics and of the immobility in ranking, the steady-state distribution of income per capita will become polarized. Since poor units are also those featuring low initial average human capital, distributions of income and education are more polarized and are geographically located ... in the periphery..., the results provide a bleak picture over the possibility of equalizing income per capita both in EU... countries in the near future.” Carrington (2006) finds evidence of convergence that is neither fast nor continuous, and that regions are persistent in belonging to certain income groups, while sub-periods of convergence and divergence are discernible during 1984-1993. Dall'erba and Le Gallo (2007) find two tiers of convergence, one in core richer regions and one in peripheral poorer regions, with the peripheral regions converging faster during 1989-1999.

At the absolute level, Cuadrado-Roura et al. (2000) find β-convergence of 2.8-3.5% during the 1981-1990 in a study of 97 European regions. However, when they look at conditional convergence, they observe three regional groups for conditional β convergence. The first group has regions exhibiting positive and significant fixed effects growing faster than others. This group includes such as Berlin, Bremen, Hamburg and Ile-de-France. The second group exhibits negative and significant fixed effects and includes regions such as UK regions (except Southeast ones), 11 Spanish regions, Portugal and Greece. The third group has near zero fixed effects and significant slowing or accelerating factors cannot be detected for this group.

Cuadrado-Roura et al. (2000) also do not find much evidence in support of the core-periphery model. However, when they disaggregate and analyze gross value added, productivity growth and employment creation, they see five emerging patterns. First, they find advanced regions with above average performance such as Hesse, Luxembourg and Lasso. Second, they identify dynamic intermediate regions such as Lombardy, Baden-Wurttemberg and Navarra, with intermediate levels of development, but dynamic in terms of productivity and employment. The third group is intermediate regions with less dynamism including regions such as Basque country, Liguria and Basse-Normandie which do not seem to be catching up. The fourth group 22