rafmacalaba/datause-encoder-data huggingface dataset:
education to access to good jobs across industries showing skills mismatches. If there were a skills-mismatch problem, industries in which the skills-mismatch is more severe, such as agriculture and low-quality services, would exhibit relatively greater contributions of educational attainment to labor market inequality, that is, workers would tend to be overeducated relative to the skills required by their jobs in these industries. The research finds that <mark>educational attainment among respondents, as a proxy for skills, does not have a greater effect on inequality in the access to good jobs in industries with more severe skills mismatches (see an</mark> nex 4, t <mark>able A4;).</mark>

# 3.3. Inequality in human capital inputs

The observed fair inequality component of the access to good jobs, that is, the component involving the effort, experience, and choice of individuals, may be affected by inequality in the access to human capital inputs among individuals during their formative years. The research extends the analysis by evaluating learning performance in Azerbaijan and Georgia based on the PISA test scores to describe access to education. The analysis also relies on the application of the HOI approach to the measurement of inequality in children’s access to basic utilities in South Caucasus. The study finds that, first, educational performance appears relatively poor and unequal depending on the life circumstances of children. Second, the coverage rates of basic human capital inputs such as access to gas and water and sanitation are generally high, although inequalities remain an issue in the countries, especially in higher-quality services. The inequalities are mostly associated with geographical disparities.

## _Learning performance_

While school attendance rates are high in the countries of South Caucasus, learning performance shows mixed results.<sup>8</sup> Only Azerbaijan and Georgia participate in the PISA test; the test was conducted in Azerbaijan in 2006 and 2009 and in Georgia in 2009 and 2015. The average PISA scores rose noticeably in Georgia between 2009 and 2015: by 38 points in science, 25 points in mathematics, and 27 points in reading (World Bank 2016) (figure 13). Because every 30-point difference in a PISA score corresponds to about one year of schooling in the 2015 round of the PISA, the improvement in Georgia is substantial. Meanwhile, in the two rounds of the PISA test in 2006 and 2009, the performance in Azerbaijan was less impressive. The country’s average score in reading rose by 9 points, but the mathematics score dropped rather significantly, by 45 points. Overall, these levels of educational achievement in the countries of South Caucasus are still far below the averages in the OECD and in Eastern Europe and Central Asia. For example, the science score in Georgia in the 2015 round of PISA was 411, while the regional average was 454, and the OECD average was 493. More than half of 15-year-olds in Georgia scored below basic proficiency in science, reading, and mathematics.

> 8 See, for example, UNESCAP (2017), which shows high attendance rates in secondary education.

25
from an OLS procedure without controls are reported in Column (4). Socio-economic background controls are added in Column (5), and educational background controls are added in Column (6).

|**Table 3A: Im**|**pact of SOF**<br>|**ES on GPA, B**<br>|**achelor student**<br>|**s, SOFES data**|||
|---|---|---|---|---|---|---|
||N|<RD|>RD||OLS||
|DS=0.08|(1)|(2)|(3)|(4)|(5)|(6)|
|±0.005|253|8.544|8.456|-0.088<br>(0.094)|-0.051<br>(0.088)|0.020<br>(0.080)|
|±0.010|733|8.504|8.430|-0.075<br>(0.052)|-0.070<br>(0.050)<br>|-0.046<br>(0.045)|
|±0.015|1,051|8.544|8.456|-0.088|-0.089<sup>**</sup>|-0.043|
|||||(0.044)|(0.042)<br>|(0.038)|
|±0.020|1,292|8.547|8.470|-0.076<br>(0.040)|-0.070<sup>*</sup><br>(0.038)|-0.024<br>(0.034)|
|DS=0.15|||||||
|±0.005|125|8.359|8.550|0.197<br>(0.130)|0.162<br>(0.130)|0.102<br>(0.107)|
|±0.010|571|8.460|8.497|0.040<br>(0.066)|0.027<br>(0.064)|-0.015<br>(0.057)|
|±0.015|683|8.459|8.503|0.046<br>(0.059)|0.036<br>(0.057)|-0.008<br>(0.050)|
|±0.020|802|8.491|8.539|0.049<br>(0.053)|0.043<br>(0.051)|0.002<br>(0.045)|
|DS=0.30|||||||
|±0.01|58|8.484|8.638|0.154<br>(0.174)<br>|0.226<br>(0.193)<br>|0.158<br>(0.163)|
|±0.02|270|8.474|8.707|0.234<sup>**</sup>|0.236<sup>**</sup>|0.109|
|||||(0.098)<br>|(0.101)<br>|(0.086)|
|±0.03|328|8.471|8.648|0.177<sup>**</sup><br>(0.082)<br>|0.165<sup>**</sup><br>(0.084)<br>|0.113<br>(0.074)|
|±0.04|395|8.478|8.604|0.126<sup>*</sup><br>(0.071)|0.136<sup>*</sup><br>(0.072)|0.082<br>(0.063)|
|_Controls_|||||||
|Socio-ec.||||No|Yes|Yes|
|background|||||||
|Educational<br>background||||No|No|Yes|

Note: Standard errors are between brackets. * indicates significance at the 10%-level, ** indicates significance at the 5%-level, and *** indicates significance at the 1%-level. Controls denoted by “socioeconomic background” include the tuition fee level, a dummy indicating whether a student receives a grant or not, family income, the socio-economic stratification index, a dummy indicating the student’s gender, the student’s age, and a dummy indicating whether the student’s family owns a real estate property. Controls denoted by “educational background” include the student’s grade point average at the highest three completed education levels, dummies indicating the type of school attended (public or private), the term of the program in which the student is enrolled, and dummies indicating the study area. DS stands for discontinuity sample and equals the cut-off points for the Economic Need Factor (ENF) where the loan <u>amount increases by additional 10% of tuition.</u> For Bachelor-students, we do not find a systematic treatment effect of student loans around DS=0.08 and DS=0.15, but the impact of credits on student performance is positive at DS=0.30. A coefficient of 0.234 20
nancial development may decline if firms reduce their demand for loans because, for example, their production falls as the economy slows down (demand channel). At the same time, a reduction in financial development may be due to the desire of financial institutions to reduce their leverage and exposure when financial conditions worsen (supply channel). We adopt two variables as possible instruments: the lagged value of financial development (assuming that only contemporaneous effects matter) and _Weighted domestic credit to private sector by banks (% of GDP)_ . The latter captures the share of domestic credit to the private sector provided by banks, and it is weighted by the ratio of foreign bank branches to total number of branches in the region. Ongena, Peydr ́o, and Van Horen (2015) uses matched firm-bank level data for Eastern Europe and Turkey to show that foreign-owned banks contracted their lending more during the financial crisis, than locally-funded domestic banks.

The results are summarized in Tables 6 and 7. The instruments appear highly correlated with the dependent variable, but not uncorrelated with the error term as we reject the null that the instruments are valid. The inclusion of the instrumental variables does not change our results.

# **4 Conclusions**

Foreign banks are important for trade as they provide finances and information to local firms. By focusing on Turkey where few firms use foreign banks to finance trade, we show the importance of the information channel that foreign banks play in promoting exports. We find that the presence of foreign banks will increase the exports of sub-regions in Turkey. The exports will further increase as the footprint or share of the foreign bank branches increases. We also show that there are spillover effects from other foreign banks of countries neighboring the trade partner: the presence of these foreign banks can increase exports to the trade-partner country but as their footprint increases, they can divert trade and reduce exports to the trade partner. The positive relationship between foreign banks and exports does not change with the financial crisis.

These results show that aside from financing, banks can play an important role in providing information and reducing information asymmetries for exporters. Questions remain on what are the mechanisms by which banks transmit the information to the exporters and whether the information from banks is unique and cannot be replicated by other agencies such as export promotion boards and chambers of commerce. We leave these questions for future research.

9
<!-- Start of picture text --> 2<br>2<br>ra<br>e<br>lo<br>O°<br>0 1 2 3 4 5 6 7 8 9 10 11 12+<br>Duration (years)<br>MEE Forced return = Normal return<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>2<br><<br>a<br>ite}<br>oO<br>4 2 0 2 4 6<br>log(Excess earnings abroad / Migration costs)<br>(99 Forced return =|“ Normal return<br><!-- End of picture text -->
|P079414|2008|2013|9/3/2012|10 Closed|Rwanda|
|---|---|---|---|---|---|
|P079414|2008|2013|5/18/2013|11 Closed|Rwanda|
|P079414|2008|2014|12/1/2013|12 Closed|Rwanda|
|P079414|2008|2014|6/15/2014|13 Closed|Rwanda|
|P079414|2008|2015|12/29/2014|14 Closed|Rwanda|
|P079552|2005|2005||0 Closed|North Macedonia|
|P079552|2005|2006|4/22/2006|1 Closed|North Macedonia|
|P079552|2005|2007|7/27/2006|2 Closed|North Macedonia|
|P079552|2005|2007|3/30/2007|3 Closed|North Macedonia|
|P079552|2005|2008|8/20/2007|4 Closed|North Macedonia|
|P079552|2005|2008|6/27/2008|5 Closed|North Macedonia|
|P079552|2005|2009|5/2/2009|6 Closed|North Macedonia|
|P079552|2005|2010|3/31/2010|7 Closed|North Macedonia|
|P079552|2005|2011|12/28/2010|8 Closed|North Macedonia|
|P079610|2006|2006|5/19/2006|1 Closed|East Asia and Pacific|
|P079610|2006|2007|6/25/2007|2 Closed|East Asia and Pacific|
|P079610|2006|2008|6/30/2008|3 Closed|East Asia and Pacific|
|P079610|2006|2009|6/30/2009|4 Closed|East Asia and Pacific|
|P079610|2006|2010|5/15/2010|5 Closed|East Asia and Pacific|
|P079610|2006|2011|6/13/2011|6 Closed|East Asia and Pacific|
|P079610|2006|2012|6/13/2011|6 Closed|East Asia and Pacific|
|P079610|2006|2020|1/7/2020|7 Closed|East Asia and Pacific|
|P079628|2005|2005|6/9/2005|1 Closed|Philippines|
|P079628|2005|2006|2/14/2006|2 Closed|Philippines|
|P079628|2005|2006|5/21/2006|3 Closed|Philippines|
|P079628|2005|2007|3/21/2007|4 Closed|Philippines|
|P079628|2005|2008|11/4/2007|5 Closed|Philippines|
|P079628|2005|2008|2/13/2008|6 Closed|Philippines|
|P079628|2005|2009|7/3/2008|7 Closed|Philippines|
|P079628|2005|2009|12/14/2008|8 Closed|Philippines|
|P079628|2005|2009|2/13/2009|9 Closed|Philippines|
|P079628|2005|2010|8/11/2009|10 Closed|Philippines|
|P079628|2005|2010|1/4/2010|11 Closed|Philippines|
|P079628|2005|2011|11/17/2010|12 Closed|Philippines|
|P079628|2005|2012|8/13/2011|13 Closed|Philippines|
|P079628|2005|2013|9/23/2012|14 Closed|Philippines|
|P079628|2005|2013|3/17/2013|15 Closed|Philippines|
|P079628|2005|2013|6/25/2013|16 Closed|Philippines|
|P079633|2005|2005|6/13/2005|1 Closed|Benin|
|P079633|2005|2006|12/21/2005|2 Closed|Benin|
|P079633|2005|2006|6/30/2006|3 Closed|Benin|
|P079633|2005|2007|12/28/2006|4 Closed|Benin|
|P079633|2005|2007|6/28/2007|5 Closed|Benin|
|P079633|2005|2008|12/14/2007|6 Closed|Benin|
|P079633|2005|2008|6/24/2008|7 Closed|Benin|
|P079633|2005|2009|12/22/2008|8 Closed|Benin|
|P079633|2005|2009|6/1/2009|9 Closed|Benin|
|East Senegal Rural Development Project|
|---|
|East Senegal Rural Development Project|
|East Senegal Rural Development Project|
|East Senegal Rural Development Project|
|East Senegal Rural Development Project|
|East Senegal Rural Development Project|
|East Senegal Rural Development Project<br>|
|HighwayProject(05)|
|HighwayProject(05)|
|HighwayProject(05)|
|HighwayProject(05)|
|HighwayProject(05)|
|Dakar Container Port Project|
|Dakar Container Port Project|
|Dakar Container Port Project|
|Dakar Container Port Project|
|Dakar Container Port Project|
|Dakar Container Port Project|
|Dakar Container Port Project|
|Dakar Container Port Project|
|Urban Management and Rehabilitation and Technical Assistance Pro|
|Urban Management and Rehabilitation and Technical Assistance Pro<br>|
|Urban Management and Rehabilitation and Technical Assistance Pro|
|Urban Management and Rehabilitation and Technical Assistance Pro|
|Urban Management and Rehabilitation and Technical Assistance Pro|
|Urban Management and Rehabilitation and Technical Assistance Pro|
|Parapublic Technical Assistance Project(02)|
|Parapublic Technical Assistance Project(02)|
|Parapublic Technical Assistance Project(02)|
|Parapublic Technical Assistance Project(02)|
|Parapublic Technical Assistance Project(02)|
|Parapublic Technical Assistance Project(02)|
|Water SupplyProject(02)|
|Water SupplyProject(02)|
|Water SupplyProject(02)|
|Water SupplyProject(02)|
|Water SupplyProject(02)|
|Water SupplyProject(02)|
|Water SupplyProject(02)|
|Water SupplyProject(02)|
|Water SupplyProject(02)|
|Irrigation Technical Assistance Project|
|Irrigation Technical Assistance Project|
|Irrigation Technical Assistance Project|
|Irrigation Technical Assistance Project|
|PrimaryEducation Development Project|
|PrimaryEducation Development Project|
Peterman, Amber. 2010. ―Widowhood and Asset Inheritance in Sub-Saharan Africa: Empirical Evidence from 15 Countries,‖ Chronic Poverty Research Centre Working Paper, Manchester, U.K.

Potash, Betty. 1986. _Widows in African Societies: Choices and Constraints_ . Stanford: Stanford University Press.

Quisumbing, Agnes, Lawrence Haddad and Christine Pena. 2001. ―Are Women Overrepresented Among the Poor? An Analysis of Poverty in 10 Developing Countries.‖ _Journal of Development Economics_ 66: 225-269.

Schultz, T. Paul. 1990. "Testing the Neoclassical Model of Family Labor Supply and Fertility," _Journal of Human Resources_ 25(4): 599-634.

Seager, Joni. 1997. _The State of Women in the World Atlas_ . New Edition. London: Penguin Reference.

Sevak, Purvi, David Weir and Robert Willis. 2003. ―The Economic Consequences of a Husband‘s Death: Evidence from the HRS and AHEAD,‖ _Social Security Bulletin_ 65(3).

Sossou, Marie-Antoinette. 2002. ―Widowhood Practices in West Africa: the Silent Victims.‖ _International Journal of Social Welfare_ , 11: 201-209.

Thomas, Duncan. 1990. "Intra-Household Resource Allocation: an Inferential Approach," _Journal of Human Resources_ 25(4): 635-664.

Wing, Susanna. 2009. ―Family Law Reform in Francophone Africa: Women‘s Rights and Legal Pluralism,‖ mimeo, Dept. of Political Science, Haverford College Wooten, Stephen. 2003. ―Women, Men, and Market Gardens: Gender Relations and Income Generation in Rural Mali,‖ _Human Organization_ 62(2): 166-177.

World Bank. 2010. Mali: Social Safety Nets, Report No. 553222-ML, Human Development Department, Social Protection Unit, Africa Region, World Bank, Washington, DC.

30
Tho difference in the maniWtude of the coefficients on import penetration and export share between the protected and unprotected sectors, although small, is quite revealing. Firms which export a larger share of their total sales have a higher productivity in the protected sectors than in t-e unprotected ones. Tbis might be due to the larger disparity, within the protected sectors, between firms which produce for the domestic market and fce little competidon, and fim wbich export and therefore bave to adjust to heavy foreign competition. Moreover, the positive effect of import penetration on productivi-y switches to a negadve effect at a lower level of import penetaton for protected sectors than for unprotected ones (the level at which the slope changes from positive to negative is obtained by setting the derivative with respect to import penetration equal to zero). The explanadon is straight forward since, although firms in both sectors do increase productivity when 02ced with import competition, firms in protected sectors, which are usualy infant industries, have less 'resistance' to competition than firms in unprotected sectors. This cannot but enforce the finding that the liberlizadon effect is indeed strong and that protection creates inefficiencies.

Another subtle difference between protected and unprotected sectors is depicted in terms of the effect of foreign share in ownership. The spillover effect of foreign investment is higher in the unprotected sectors than in the protected ones, as shown by the coefficients on SFORSH. This suggests that, since protected firms usually have less incentive for being more efficient because they are shielded from external competition, they wiUl be less responsive to any transfer in technology brought about by foreign-owned firms. Moreover, the coefficient on FORSH is also higher in unprotected sectors than in protected ones, suggesting that even foreign-owned firms take advantage of the protective regime and enjoy a quiet life.

The Chow test was performed to statistically test whether or not the parameter values associated with the protected sectors (based on the tariff criterion only) are the same as those associated with the 22
# **Appendix 4: Estimations by Domain and Sensitivities**

|**Table A4.1 Convergence Estim**<br>**Category**|**ated by Domain**<br>**Model No.**|**Calibration**|
|---|---|---|
||1.1|Closure|
|Education|1.2|SSA|
||1.3|OECD|
||2.1|net-reproduction rate|
|Fertility|2.2|SSA|
||2.3|OECD|
|LFPR|3.1|Closure|
|PG|4.1|Closure|
|ay-ap|4.2|OECD|
|Sectoral|5.1|Closure|
||5.2|Closure+|
|Non-Tradable|6.1|Additive of NTP|
|LFPR Pa-Ga and Sectoral|7.1|Closure|
|, yp|7.2|Closure+|
||7.3|Closure|
|Complete Convergence|7.4|Closure+|
|_Notes:_Closure represents comple|7.5<br>te convergence of the re|Additive of NTP<br>spective gap. Closure+ represents|

_<mark>Notes:</mark>_ <mark>Closure represents complete convergence of the respective gap. Closure+ represents</mark> appreciation/depreciation is applied to wages due to the displacement of workers between sectors. SSA and OECD stands for benchmarking with averages found in Sub-Saharan Africa and OECD countries, respectively. Additive represents the additional value added by the non-tradable sector which is summed to the status-quo.

|**Table A4.2 Sensi**|**tivity Tests**||
|---|---|---|
|**Model of Appl.**|**Description**|**Value**|
|1.1|Economic returns to schooling|4.2%|
|1.1|Economic returns to schooling|12.5%|
||Effect of fertility on female labor||
|3.1|supply|0|
|6.1|Non-tradable wages|depreciated|

_<mark>Notes:</mark>_ <mark>refer to Appendix 4 for the status-quo values of coefficients.</mark> 36
Srinivasan, Krishna (1999). "Foreign direct investment in Africa: some case studies" (Washington D.C.: International Monetary Fund), mimeo.

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_(1999). Foreign Direct Investment in Africa: Performance and Potential_ (New York and Geneva: United Nations), United Nations publication, UNCTAD/ITE/IIT/Misc. _15._

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- _Investment du Mali_ (New York and Geneva: United Nations; Paris: ICC).

- Wells, Louis T. (2000). "Cutting red tape: lessons from a case-based approach to improving the investment climate in Mozambique" in Administrative Barriers to Foreign Investment by Jarnes Emery, Melvin Spence, Louis Wells and Timothy Buehrer, FIAS Occasional Paper N. 14, Washington, DC., 2000.

- Wheeler, David and A. Mody (1992). "International investment location decisions: the case of U.S. firms", _Journal of International Economics,_ 33, pp. 57-76.

World Bank (1999). _World Development Report_ (Washington D.C.: World Bank).

World Economic Forum (1998). _The Africa Competitiveness Report,_ World Economic Forum, Geneva, Switzerland 20
developing countries concentration is smaller than for the developed countries in the sample, such as Norway, Sweden, and New Zealand. Similarly, Bernard, Jensen, and Schott (2009) and Mayer and Ottaviano (2008) show that the United States (U.S.) and other European countries exhibit high concentration in the top firms.

The measures of exporter dynamics also exhibit important variability across countries. Entry rates–defined as the number of exporters in year _t_ not present in year _t-1_ , over the total number of exporters in year _t_ —range from 22 percent in Brazil to more than 50 percent in Malawi, Yemen, the Lao People’s Democratic Republic and Tanzania. Exit rates–defined as the number of exporters in year _t-1_ not present in year _t_ , over the total number of exporters in year _t-1_ —range from 22 percent in Bangladesh to 61 percent in Malawi.<sup>9</sup> First-year survival rates of entrants– defined as the number of exporters in year _t_ and _t+1_ not present in year _t-1_ , over the total number of exporters in year _t_ —vary between 23 percent in Cameroon and 61 percent in Bangladesh. The magnitude of the survival rates in Table 1 suggests an extremely high attrition rate of new entrants after just one year in export markets, particularly in Africa.

Table 2 presents the same set of measures but for groups of HS 2-digit products – henceforth designated as sectors - where the measures for each group of sectors are obtained as averages across all countries that export that particular sector, again focusing on the period 20062008. Table 2 shows a less pronounced variation in the number of exporters across sectors though

> African countries exhibit very fat-tailed distributions of export value across products where export big hits are uncommon but are much more common than would be expected in a normal distribution whereas Hanson, Lind, and Muendler (2014) document a strong empirical regularity across countries in their patterns of comparative advantage which is hyper-specialization in exporting, the fact that in any given year, exports in the typical country tend to be highly concentrated in a small number of industries. 9 These rates of entry into and exit from export markets are substantially larger than the rates of entry into and exit from manufacturing activity reported by Bartelsman, Haltiwanger, and Scarpetta (2013). Focusing on census/registries of manufacturing firms (excluding 1-employee businesses) they document turnover of 10-20 percent in industrial countries and somewhat higher in developing countries.

8