rafmacalaba/datause-encoder-data huggingface dataset:
for the median driver/voter obtained by Ashenfelter and Greenstone (2004) using mandated speed limits and the median value of 5 million dollars used by Murphy and Topel (2001). From both estimates, I get the value of a life-year at each age (see fi gure 1a in Murphy and Topel 2001). Even though no estimates of the value of life in Uganda or in Africa are readily available, I have tried to account for the fact that the value of life is greater than the present discount value of income streams. In order to translate the U.S. estimates to Ugandan levels, I calculated the ratio between the value of a life-year and the income for both estimates in the U.S., and applied the same ratio to the Ugandan incomes.

The value of life in the model of the internal rate of return,<sup>25</sup> is introduced by adding to the earnings of high school and graduates at each age, the part of the value of a life-year that exceeds labor income. More precisely, I subtract from the value of a life-year at each age the average labor income at that age, to obtain an estimate: θt = full utility at age t — average labor income at age t For simplicity, and because it is difficult to estimate the skill premium outside the labor markets, I assume that θt is the same for both education groups, and I add it, at each age, on both sides of equation of the internal rate of return.

The results, reported in table A1, vary depending on the estimates of the value of life. When including the value of life in the calculations, returns to education go from a benchmark of 10.23 percent to a range between 11.54 percent and 13.74 percent.

Counting only future income streams, the effect of education in reducing HIV/AIDS prevalence adds 0.48 percentage point to the standard estimates of the returns to education. When adding estimates of the value of life, the addition to the returns to schooling varies between 1.31 and 3.51 percentage points. These additions are substantial.<sup>26</sup> They indicate that, in the presence of an epidemic that kills early in life and in large proportions, the nonmarket, health related, returns to schooling can be important. However, it should be kept in mind that these calculations assume that the effect of education in reducing the risk of HIV infection is causal, a point that has not been conclusively established by this paper.

> 25 I have bene fi ted from discussions with Fabian Lange on this subject.

> 26 They are orders of magnitude larger than what I calculated for the effect of education in reducing smoking prevalence in the U.S. In that case the addition was between 0.1 and 0.5 percentage point, See De Walque (2003)

32
3. The paper is organized as follows. The first section describes the strategy we employed to identify the research components and studies. The second section presents our findings with respect to (i) the volume of studies and compares these results with the erlier study, (ii) the type of studies reported, and (iii) the budgeted cost of the studies. The third section examines preconditions of study completion, using data from Staff Appraisal Reports (SARs) and interview9 with selected Project Officers. The final section discusses the findings and draws conclusions.

# IL. Methodology

# Volume of Research

4. A list of all education projects initiated since 1982 was compiled from annual analyses of World Bank lending for education and training.<sup>6</sup> The project d:scnption and objectives, agreements, recommendations and annexes of Staff Appraisal Reports (SAR) for each project was examined for evidence that the project contained a research component. All research components so identified were then listed and, to thN extent possible, planned studies were classified by design.

_5._ Projects were then grouped by Bank region and survey forms sent to division chiefs of the 19 sub-regions with a memo asking that they be completed by appropriate staff and returned. 7 The surveys asked staff to indicate the status of the listed studies, provide information on studies not included, and confirm those projects which were indicated as containing no research component. They were also asked to identify documents relating to completed studies. A list of all projects and research components with the comments of regional staff, quoted verbatim, and available documents is listed in Annex 1.

6. A list of studies identified in the Tan (1982) document, but rernaining incomplete as of 1982, was also sent to regional staff, with the same request. A list of these projects and research components with regional staff comments is listed in Annex 2.

7. Where regional staff were unable to provide information 8 Project Completion Reports (PCR), where available, were scrutinized for results of previously indicated studies<sup>9</sup> . Information from PCRs is also included within the list of projects. However, PCRs did not provide information on specific documents used in their analysis of

> 6 EDC, 1981-7, PHREE, 1987-89

> **7 See Annex** 4 for the list of Division Chiefs **and the** accompanying memorandum.

> 8 There **wa,** an 84% response to the **memo** sent to division chiefs. **The** shornfall is **primarily** accounted for by **ASS** (Indonesia in particular) and LA4. **The Reorgsniaruon has** resulted in some instiutional memory loss, although respondents were able to provid: information on **the** status of 90% of indicated studies.

> 9 Project Completior Repons were available for 19% of the projects Some were completed before the results of studies were released. others did not provide detai ot **swdy success.** No PCR provided bibliographic information relating to project studies

2
# **Figure 15: Labor force participation by race, immigration status and region of origin for Blacks. Males.**

<!-- Start of picture text --> 0<br>-0.02<br>-0.04<br>-0.06<br>-0.08<br>-0.1<br>-0.12<br>Whites out Blacks inBlacks AfricaBlacks LACBlacks other Asians inAsians outHispanics inHispanics out<br><!-- End of picture text -->

**Figure 16: Labor force participation by race immigration status and region of origin for Blacks. Females.**

<!-- Start of picture text --> 0.08<br>0.06<br>0.04<br>0.02<br>0<br>-0.02<br>-0.04<br>-0.06<br>-0.08<br>-0.1<br>-0.12<br>Whites out Blacks inBlacks AfricaBlacks LACBlacks other Asians inAsians outHispanics inHispanics out<br><!-- End of picture text --> Data from the 2000 Census IPUMS data (5% sample), population aged 25-65. The figures display the marginal coefficients and their 95% confidence intervals on interaction terms for racial groups and foreign born status (“in” for natives, “out” for foreign born) in probit regressions with labor force participation as the dependent variable. “White natives” is the omitted dummy. “Blacks Africa” includes Blacks born in Africa, “Blacks LAC” includes Blacks born in Latin America and the Caribbean and “Blacks other” those born elsewhere, but not in the United States.

28
28 Table 6 Pensions and total cash social transfers as percent of GDP

||||Transfer<br>|s<br>|
|---|---|---|---|---|
|e|e<br>||||
|E<br><br>|e<br><br><br>||||
|e<br><br>|e<br><br><br><br>|<br>|<br>|<br><br>|
|T<br>|t<br>|<br>|se <br>|to<br>|e<br><br>||
|Kyneton<br><br>||<br><br><br>|<br>|<br>|<br><br>|
|e<br><br>|e<br><br><br>||||
||os ||st|||
|froma<br><br>||<br><br><br>|ls <br><br><br>|F<br><br><br>|w<br> <br><br>|
||<br><br><br><br>|<br><br>|<br>|<br><br>F<br>||<br> <br>t<br>te|
||ee|<br>|<br><br>|<br><br>|
|[siwvi<br>|ceMow<br>|<br>gt||ove<br>ce|P<br>||t<br>to||

Note: Regional means are unweighted averages. Source: World Bank PRDTE data files. Total cash transfers include pensions, various family benefits, sick leave benefits (including maternity leave) and unemployment allowances.

a = includes only pensions; b = only pensions and unemployment benefits; c = only pensions and sick leave benefits;
18 Corruption proved to have fewer statistically significant effects on affordability than on access during the period of analysis (line 16). One exception stands out. On average, an increase in corruption was associated with an increase in the price of a local phone call. Because it has no effects on the fixed connection cost, it is likely that the impact is on variable costs and interconnection rates.

**<u>Table 8 Econometric results for affordability, 1990–2002</u>**

|||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|---|
|||**Ln**<br>**electricity**<br>**price for**<br>**households**<br>**(2000 U.S.**<br>**cents/kWh)**|**Ln**<br>**electricity**<br>**price for**<br>**industry**<br>**(2000 U.S.**<br>**cents/kWh)**|**Ln price of**<br>**local**<br>**telephone**<br>**call (2000**<br>**U.S.**<br>**cents/3-min**<br>**call)**|**Ln monthly**<br>**residential**<br>**subs. fee**<br>**(2000 U.S.$)**|**Ln monthly**<br>**business**<br>**subs. Fee**<br>**(2000 U.S.$)**|
|**(1)**|IRA|–0.110|–0.138|0.064|0.479**|0.177|
|||[0.85]|[1.21]|[0.38]|[2.54]|[0.95]|
|**(2)**|Privatization|0.437***|0.310***|0.738***|0.505***|0.465***|
|||[3.35]|[2.68]|[5.16]|[3.08]|[2.91]|
|**(3)**|IRA *|–0.001|–0.147**|–0.178**|–0.101|–0.068|
||Privatization|[0.01]|[2.30]|[2.37]|[1.18]|[0.82]|
|**(4)**|Corruption index|–0.159|0.035|0.756***|0.356*|0.217|
||(0=low 1=high)|[0.69]|[0.17]|[4.43]|[1.81]|[1.13]|
|**(5)**|IRA * corruption|0.033|0.365*|0.341|–0.644**|–0.162|
|||[0.13]|[1.67]|[1.29]|[2.12]|[0.54]|
|**(6)**|Privatization *<br>corruption|–0.103<br>[0.38]|–0.345<br>[1.45]|–1.583***<br>[6.74]|–0.358<br>[1.47]|–0.402*<br>[1.68]|
|**(7)**|Ln GDP per capita<br>(2000 U.S.$)|0.636***<br>[2.68]|1.305***<br>[6.19]|–0.454**<br>[2.28]|–0.816***<br>[3.93]|–0.441**<br>[2.17]|
|**(8)**|Ln urbanization|0.264|4.303***|0.154|–3.538***|–1.071**|
|||[0.27]|[4.95]|[0.36]|[7.19]|[2.18]|
|**(9)**|Time trend|0.003|–0.063***|–0.058***|0.027**|–0.019*|
|||[0.32]|[7.38]|[6.16]|[2.46]|[1.73]|
|**(10)**|Constant|–7.348|128.654***|120.283***|–36.570*|48.241**|
|||[0.37]|[7.24]|[6.83]|[1.82]|[2.47]|
|**(11)**|Observations|380|380|943|968|957|
|**(12)**|R–squared within|0.33|0.22|0.34|0.15|0.09|
|**(13)**|No. of countries in<br>|47|47|91|91|91|
||sample|**_M_**|**_arginal effe_**|**_cts_**|||
|**(14)**|IRA|–0.094**<br>[1.98]|–0.007<br>[0.18]|0.183***<br>[3.45]|0.112<br>[1.79]|0.071<br>[1.16]|
|**(15)**|PRIV|0.385***<br>[7.41]|0.055<br>[1.19]|–0.153***<br>[2.89]|0.276***<br>[4.52]|0.228***<br>[3.83]|
|**16**|CORRUPTION|–0.176|0.125|0.394***|–0.022|0.020|
|**()**||[1.04]|[0.83]|[2.74]|[0.13]|[0.13]|

Absolute value of t statistics in brackets. * significant at 10 percent, ** significant at 5 percent, *** significant at 1 percent.

The interaction dummies suggest that in general IRA and PPI do not systematically interact to influence prices (line 3). This is quite surprising, because it is
# **Horizontal Depth:**

# **A New Database on the Content of Preferential Trade Agreements**<sup>1</sup>

**Claudia Hofmann, Alberto Osnago and Michele Ruta**

World Bank _Keywords_ : Preferential Trade Agreements, Deep Integration, Legally Enforceable Provisions _JEL Codes_ : F02, F13, F15

> 1 We are grateful to our legal consultants on this project, the law firm Batalla Salto Luna, and particularly Priscilla Ortiz for the excellent work in coding the preferential trade agreements. We would like to thank Rohini Acharya, Alen Mulabdic and Robert Teh and seminar participants at Stanford University, the World Bank, and EIEF (Rome) for comments on an earlier draft. Errors and omissions are our responsibility only.
_Sozialpolitik_ , 56(3): 573–609.

- Kondratieff, N. and W. Stolper (1935). “The Long Waves in Economic Life.” _The Review of Economics and Statistics_ , 17(6): 105-115.

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- Kose, M.A. (2002). “Explaining Business Cycles in Small Open Economies: How Much do World Prices Matter?” _Journal of International Economics_ , 56(2): 299–327.

- Kuznets, S. (1930 _). Secular Movements in Production and Prices. Their Nature and their Bearing upon Cyclical Fluctuations_ . Boston: Houghton Mifflin.

- Labys, W.C., A. Achouch, and M. Terraza (1999). “Metal Prices and the Business Cycle.” _Resources Policy_ , 25: 229-238.

- Labys, W.C., J.B. Lesourd, and D. Badillo (1998). “The Existence of Metal Price Cycles.” _Resources Policy_ , 24(3): 147-155.

- Malthus, T.R. (1798). _An Essay on the Principle of Population_ . London: J. Johnson.

- Marañon, M. and M. Kumral (2019). “Dynamics Behind Cycles and Co-movements in Metal Prices: An Empirical Study Using Band-Pass Filters.” _Natural Resources Research_ 29(3): 1487-1519.

- McCalla, A.F. (1999). “Prospects for Food Security in the 21<sup>st</sup> Century with Special Emphasis on Africa.” _Agricultural Economics_ , 20: 95-103.

- Mendoza, E. (1995). “The Terms of Trade, the Real Exchange Rate, and Economic Fluctuations.” _International Economic Review_ , 36: 101-137.

- Mitchell, W.C. (1908). _Gold, Prices, and Wages Under the Greenback Standard_ . Berkeley, CA: University of California Press.

- Nelson, C.R. and C.I. Plosser (1982). “Trends and Random Walks in Macroeconomic Time Series: Some Evidence and Implications.” _Journal of Monetary Economics_ , 10: 139-l 62.

- Ojeda-Joya, J.N., O. Jaulin-Mendes, and J.C. Bustos-Pelaez (2019). “The Interdependence between Commodity Price and GDP Cycles: A Frequency-domain Approach.” _Atlantic Economic Journal_ , 47: 275-292.

- Phillips, P.C.B. and S. Jin (2015): “Business Cycles, Trend Elimination, and the HP Filter.” Cowles Foundation Discussion Paper 2005. New Haven, CT: Yale University.

- Phillips, P.C.B. and Z. Shi (2019). “Boosting: Why You Can Use the Hodrick-Prescott Filter?” Cowles Foundation Discussion Paper 2192. New Heaven, CT: Yale University.

- Prebisch, R. (1950). _The Economic Development of Latin America and its Principal Problems_ , New York: United Nations.

- Radetzki, M., R.G. Eggert, G. Lagos, M. Lima, and J.E. Tilton (2008). “The Boom in Mineral Markets: How Long Might It Last?” _Resources Policy_ , 33: 125-128.

- Reinhart, C.M., V. Reinhart, and C. Trebesch (2016). “Global Cycles: Capital Flows, Commodities, and Sovereign Defaults.” _American Economic Review Papers and Proceedings_ ,

_— 31 —_
|P122594|2012|2014|9/5/2013|3 Closed|Yemen,Republic of|
|---|---|---|---|---|---|
|P122594|2012|2014|11/30/2013|4 Closed|Yemen,Republic of|
|P122594|2012|2014|5/20/2014|5 Closed|Yemen,Republic of|
|P122594|2012|2015|10/11/2014|6 Closed|Yemen,Republic of|
|P122594|2012|2015|5/26/2015|7 Closed|Yemen,Republic of|
|P122594|2012|2016|12/1/2015|8 Closed|Yemen,Republic of|
|P122594|2012|2016|6/6/2016|9 Closed|Yemen,Republic of|
|P122594|2012|2017|11/21/2016|10 Closed|Yemen,Republic of|
|P122601|2011|2011||0 Closed|Somalia|
|P122601|2011|2012|11/23/2011|1 Closed|Somalia|
|P122601|2011|2013|11/23/2011|1 Closed|Somalia|
|P122601|2011|2014|8/13/2013|2 Closed|Somalia|
|P122601|2011|2014|4/14/2014|3 Closed|Somalia|
|P122616|2011|2011||0 Closed|Malawi|
|P122616|2011|2012|9/22/2011|1 Closed|Malawi|
|P122616|2011|2012|6/29/2012|2 Closed|Malawi|
|P122616|2011|2013|1/5/2013|3 Closed|Malawi|
|P122616|2011|2014|7/6/2013|4 Closed|Malawi|
|P122616|2011|2014|12/29/2013|5 Closed|Malawi|
|P122616|2011|2014|6/25/2014|6 Closed|Malawi|
|P122616|2011|2015|1/15/2015|7 Closed|Malawi|
|P122616|2011|2016|9/8/2015|8 Closed|Malawi|
|P122616|2011|2016|2/19/2016|9 Closed|Malawi|
|P122616|2011|2017|9/9/2016|10 Closed|Malawi|
|P122616|2011|2017|4/23/2017|11 Closed|Malawi|
|P122616|2011|2018|3/16/2018|12 Closed|Malawi|
|P122616|2011|2018|6/15/2018|13 Closed|Malawi|
|P122622|2010|2011||0 Closed|Sierra Leone|
|P122622|2010|2012|9/19/2011|1 Closed|Sierra Leone|
|P122622|2010|2012|12/3/2011|2 Closed|Sierra Leone|
|P122622|2010|2012|3/25/2012|3 Closed|Sierra Leone|
|P122622|2010|2013|8/27/2012|4 Closed|Sierra Leone|
|P122622|2010|2013|5/15/2013|5 Closed|Sierra Leone|
|P122622|2010|2014|1/15/2014|6 Closed|Sierra Leone|
|P122622|2010|2020|2/26/2020|7 Closed|Sierra Leone|
|P122629|2013|2013||0 Active|Vietnam|
|P122629|2013|2014|10/23/2013|1 Active|Vietnam|
|P122629|2013|2015|7/19/2014|2 Active|Vietnam|
|P122629|2013|2015|1/29/2015|3 Active|Vietnam|
|P122629|2013|2016|7/30/2015|4 Active|Vietnam|
|P122629|2013|2016|2/10/2016|5 Active|Vietnam|
|P122629|2013|2017|10/11/2016|6 Active|Vietnam|
|P122629|2013|2017|5/23/2017|7 Active|Vietnam|
|P122629|2013|2018|9/13/2017|8 Active|Vietnam|
|P122629|2013|2018|4/16/2018|9 Active|Vietnam|
|P122629|2013|2019|8/7/2018|10 Active|Vietnam|
|P122629|2013|2019|2/22/2019|11 Active|Vietnam|
(6) F Ss s oO ic = Pio it tV it-1 +i t+ Eit it=I ja Sit< nn<o= —% 9 =O y= +o POit =FIAOn it-1) = A(u; —Bi oe -M it-1- ni) — A(Hj-1 —Bi ite -M it-1- Nd) AG) ij
**Table 6: Maize production and consumption ratios and change in NBR from lower price during an**

**export ban**

|||**Panel A**|**: Characteri**|**stics across v**|**arious grou**|**ps of HHs**|||
|---|---|---|---|---|---|---|---|---|
|Group|PR||CR|N|BR|Net-|Net-|Self-<br>|
|||||||||sufficient|
|||||**_(as % of c_**|**_onsumption)_**|buyers|sellers|<br>|
|||||||||HHs|
|||||(for 1%|(for a 6%||||
||(as % o|f HH ex|penditure)|<br>decrease<br>in prices)|<br>decrease in<br>prices)||(% of househ|olds)|
|_Residence_|||||||||
|Urban|2.82||12.85|0.10|0.59|80.4|5.6|14.0|
|Rural|11.19||25.82|0.15|0.86|60.9|13.1|26.0|
|_Region_|||||||||
|North|7.66||19.85|0.12|0.72|62.2|10.5|27.3|
|Central|12.82||24.08|0.11|0.66|63.2|17.0|19.8|
|South|6.91||23.35|0.16|0.97|66.4|6.7|26.8|
|_Poverty status_|||||||||
|Non-poor|8.64||17.93|0.09|0.55|68.6|14.6|16.7|
|Poor|10.78||30.06|0.19|1.14|59.7|7.9|32.4|
|_Urban by Con_|_sumption Q_|_uintile_|||||||
|Lowest|5.22||20.12|0.15|0.88|77.0|4.2|18.8|
|2|2.92||15.33|0.12|0.73|77.6|5.2|17.2|
|3|2.08||12.28|0.10|0.60|82.3|5.0|12.7|
|4|2.16||10.33|0.08|0.48|84.3|3.8|11.9|
|Highest|1.70||6.12|0.04|0.26|81.0|9.7|9.3|
|_Rural by Cons_|_umption Q_|_uintile_|||||||
|Lowest|11.90||27.90|0.16|0.94|57.0|10.4|32.6|
|2|11.06||30.42|0.19|1.14|59.5|8.9|31.6|
|3|11.12||28.25|0.17|1.01|60.2|10.5|29.3|
|4|11.33||24.51|0.13|0.78|64.5|14.4|21.1|
|Highest|10.56||18.02|0.07|0.44|63.4|21.1|15.5|
|**Malawi**|9.60||23.35|0.14|0.81|64.6|11.6|23.7|
|||**Pane**|**l B: NBR am**|**ong net-sell**|**ers and net-**|**buyers**|||
|||Net-b|uyers of maize|||Net-selle|rs of maize||
||**PR**|**CR**|**NBR**|**NBR**|**PR**|**CR**|**NBR**|**NBR**|
||||(1% price|(6% price|||(1% price|(6% price|
||||<br>decrease)|<br>decrease)|||<br>decrease)|<br>decrease)|
|_Residence_|||||||||
|Urban|0.01|0.13|0.12|0.70|0.22|0.10|−0.12|−0.73|
|Rural|0.07|0.28|0.20|1.21|0.33|0.18|−0.15|−0.88|
|_Region_|||||||||
|North|0.07|0.26|0.19|1.14|0.29|0.16|−0.13|−0.74|
|Central|0.09|0.29|0.20|1.17|0.35|0.19|−0.17|−0.98|
|South|0.06|0.27|0.21|1.25|0.28|0.16|−0.12|−0.69|
|**Malawi**|0.06|0.24|0.18|1.09|0.32|0.17|−0.15|−0.88|

Source: Authors’ calculations based on IHS4 data. NBR is estimated based on equation 11. Self-sufficient households would neither gain nor loss due to change in maize prices.

26
Table 7: Import Uncertainty and Export Dynamics: The Role of Sunk Costs

||Firm Entry<br>Rate<br>(1)|Firm Exit<br>Rate<br>(2)|First yr.<br>Survival<br>(3)|Second yr.<br>Survival<br>(4)|Third yr.<br>Survival<br>(5)|
|---|---|---|---|---|---|
|Log import uncertainty|0.005|0.020*|-0.066***|-0.103***|-0.093*|
||(0.009)|(0.011)|(0.021)|(0.032)|(0.056)|
|Log import uncertainty _×_<br>|-0.002<br>|-0.009**<br>|0.011*<br>|0.020**<br>|0.009<br>|
|Log importer entry cost (% GNI)|(0.003)|(0.004)|(0.007)|(0.010)|(0.017)|
|Log importer entry cost (% GNI)|0.017|0.006|-0.035|-0.063|-0.134|
||(0.015)|(0.019)|(0.037)|(0.058)|(0.089)|
|Log direct imports (%)|-0.014|-0.004|-0.026|-0.022|0.061|
||(0.011)|(0.015)|(0.031)|(0.053)|(0.093)|
|Log direct exports (% sales)|-0.027***|-0.032***|0.106***|0.114***|0.089|
||(0.005)|(0.013)|(0.028)|(0.040)|(0.062)|
|Log experience|0.001|-0.005|0.117***|0.161***|0.128|
||(0.013)|(0.016)|(0.035)|(0.058)|(0.104)|
|Log nb. of employees|-0.020*|-0.026***|0.080***|0.116***|0.132**|
||(0.010)|(0.009)|(0.019)|(0.029)|(0.057)|
|Log TFP|0.043*|-0.046**|0.176***|0.311***|0.269***|
||(0.024)|(0.019)|(0.039)<br>|(0.063)|(0.101)|
|Common border|-0.033|-0.040|0.065*|0.066|-0.022|
||(0.022)|(0.027)|(0.039)|(0.059)|(0.090)|
|Colony|-0.078*|-0.019|0.147**|0.217*|0.078|
||(0.041)|(0.041)|(0.073)|(0.112)|(0.284)|
|Log distance|0.110***|0.035***|-0.069***|-0.108***|-0.150***|
||(0.009)|(0.010)|(0.018)|(0.029)|(0.051)|
|Trade agreement|-0.064***|-0.036**|0.099***|0.163***|0.196**|
||(0.016)|(0.018)|(0.033)|(0.051)|(0.082)|
|Log tarifs|0.008|0.012*|-0.027**|-0.028*|-0.030|
||(0.005)|(0.006)|(0.011)|(0.017)|(0.027)|
|Log exporter GDP cap.|-0.074|0.055|-0.047|-0.190|2.719*|
||(0.097)|(0.117)|(0.181)|(0.261)|(1.638)|
|Log importer GDP cap.|0.061|0.004|0.125|0.143|0.263|
||(0.041)|(0.049)|(0.098)|(0.152)|(0.229)|
|Log exporter entry cost (% GNI)|-0.021|-0.078*|-0.141*|-0.327***|0.158|
||(0.036)|(0.043)|(0.084)|(0.121)|(0.400)|
|Constant|-1.046|-1.161|-1.180|-0.472|-26.023*|
||(0.863)|(1.033)|(1.641)|(2.428)|(14.805)|
|Observations<br>|14,476|12,001|11,834|10,755|5,680|
|R<sup>2</sup>|0.182|0.183|0.106|0.100|0.123|
|Country Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Destination Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Sector Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Year Fixed Efects|Yes|Yes|Yes|Yes|Yes|

Notes: PPML estimates. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors clustered on exporter-importer pair in parentheses. Log direct imports (%), Log direct exports (% sales), Log experience, Log nb. of employees and Log TFP computed over the sample of exporting firms, except in Column 1. The description and source of variables are provided in Table A3 in the Appendix.

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