rafmacalaba/datause-encoder-data huggingface dataset:
**Table A2.1 Derivation of country level weights for analysis of incidence**

||_PopulationinDHS_<br>_sample_|_Populationincountry_<br>_(inthousands)_|_Percentageofpopulation_<br>_sampled_|_Relativeweight_|
|---|---|---|---|---|
||(1)|(11)|(I)as a percentageof(11)||
|Benin1996|27,892|5,632|0.495|0.367|
|BurkinaFaso1998|32,181|10,996|0.293|0.621|
|C.A.R.1994-95|28,050|3,254|0.862|0.211|
|Cameroon1998|26,523|14,303|0.185|0.979|
|Chad1996|37,213|6,937|0.536|0.339|
|C6ted'lvoire1994|38,783|13,132|0.295|0.615|
|Ghana1998|22,625|18,460|0.123|1.482|
|Mali1995-96|50,159|9,849|0.509|0.357|
|Niger1997|36,722|9,799|0.375|0.485|
|Nigeria1999|38,558|123,897|0.031|5.836|
|Senegal1992-93|31,966|7,800|0.410|0.443|
|Togo1998|44,157|4,345|1.016|0.179|
|**WesternandCentral**|||||
|**Africa**|**414,829**|**228,404**|**0.182**|**1.000**|
|Comoros1996|14,297|504|2.837|0.077|
|Kenya1998|37,705|28,612|0.132|1.656|
|Madagascar1997|35,059|14,148|0.248|0.881|
|Malawi1996|12,597|8,986|0.140|1.557|
|Mozambique1997|44,822|16,630|0.270|0.810|
|Rwanda1992|31,881|7,350|0.434|0.503|
|Tanzania1996|40,220|30,488|0.132|1.654|
|Uganda1995|36,026|19,168|0.188|1.161|
|Zambia1996-97|39,721|9,214|0.431|0.506|
|Zimbabwe1999|28,523|11,904|0.240|0.911|
|**Easternand Southern**|||||
|**Africa**|**320,851**|**147,004**|**0.218**|**1.000**|

_Source:_ Population data from World Development Indicators (World Bank, 1999).

**Table A2.2 Derivation of country level weights for analysis of treatment**

||_PopulationinDHS_<br>_sample_|_Populationincountry_<br>_(inthousands)_|_Percentageofpopulation_<br>_sampled_|_Relativeweight_|
|---|---|---|---|---|
||(I)|(11)|(I)asapercentageof(11)||
|BurkinaFaso1992/3|34,222|9,198|0.372|0.312|
|Cameroon1991|20,724|11,797|0.176|0.660|
|C6ted'lvoire1994|38,783|13,132|0.295|0.393|
|Ghana1993|22,139|16,200|0.137|0.849|
|Niger1992|34,297|8,261|0.415|0.279|
|Nigeria1999|38,558|123,897|0.031|13.938|
|Senegal1992/3|31,966|7,800|0.410|0.283|
|**WesternandCentral**|||||
|**Africa**|**220,689**|**190,285**|**0.116**|**1.000**|
|Kenya1998|37,705|29,295|0.129|1.661|
|Malawi1996|12,597|8,987|0.140|1.525|
|Rwanda1992|31,881|7,350|0.434|0.493|
|Madagascar1992|31,423|12,202|0.258|0.830|
|Tanzania1991/2|46,733|26,691|0.175|1.221|
|Zambia1992|34,943|8,262|0.423|0.505|
|Zimbabwe1999|28,523|11,904|0.240|0.892|
|**Easternand Southern**|||||
|**Africa**|**223,805**|**104,690**|**0.214**|**1.000**|

_Source: Population data from World Development Indicators (World Bank, 1999)_ _37_
Table 2: Net gains from investing along the Dakar-Lagos corridor (in %)

||(1)Fro|m road investm|ents only||
|---|---|---|---|---|
|Costs|Total welfa|regains|Net total|gains|
||Countries along<br>the corridor|All countries|Countries along<br>the corridor|All countries|
|Low bound|0.66|0.57|0.60|0.51|
|High bound|0.66|0.57|0.50|0.42|
|(2)With r|eduction of border|transit delays a|longthe Dakar-Lag|os corridor|
|Ct|Total welfa|regains|Net total|gains|
|oss|Countries along<br>the corridor|All countries|Countries along<br>the corridor|All countries|
|Low bound|1.45|1.31|1.39|1.25|
|High bound|1.45|1.31|1.29|1.16|
|(3)|With reduction of|border transit|delays for all count|ries|
|Ct|Total welfa|regains|Net total|gains|
|oss|Countries along<br>the corridor|All countries|Countries along<br>the corridor|All countries|
|Low bound|1.74|1.31|1.68|1.87|
|High bound|1.74|1.31|1.58|1.78|
||(4)With addition|al reduction in|fnal border delays||
|C|Total welfa|regains|Net total|gains|
|osts|Countries along<br>the corridor|All countries|Countries along<br>the corridor|All countries|
|Low bound|3.17|3.63|3.11|3.49|
|High bound|3.17|3.63|3.01|3.49|

Note: Authors’ calculations.

# **4.4.3 Gains at the sub-national level**

The simulations allow to present income gains at the sub-national levels. Figure 5 shows the sub-national gains for investing in the whole Dakar-Lagos corridor. Figure 6 shows the gains for additionally removing transit delays along the corridor only. Figure 7 shows the gains from removing all transit delays and reducing final border delays by half.

Improving a section of the transport network will translate into lower costs for more regions than those directly located along the corridor. Using GIS methods to compute new times between all pairs of locations allows to consider the indirect impacts of corridor improvement all sub-national locations in the region. Figure 5 shows that the locations that gain the most are those located along the corridors. However not all of them do, and some distant locations also benefit from lower transport costs. The model includes both direct and indirect impacts when simulating the welfare gains.

Reducing transit delays along the corridor brings large income gains for most locations 18
nological innovation on employment can be ambiguous. While product innovation is typically aimed at increasing a firm’s demand through the introduction of a new product, process innovation by definition usually entails productivity enhancements that can be labor saving. The existence of competing mechanisms make the net effects uncertain. For instance, in the case of product innovation, even though a new product is associated with new demand and new markets, there remains the possibility that the new product may be produced more efficiently than old products. While in the short term it is possible that new product sales may cannibalize old ones, the extent to which this occurs will depend on the degree to which the new products are substitutes or complements in demand and production. Furthermore, the net effect will depend on the extent that competitors react by introducing new products themselves, resulting in dynamic competition. For process innovations, while the use of new technologies and capital may be labor saving, under competitive conditions where lower costs of production translate to lower final goods prices, increases in demand and sales may offset any initial labor displacement. Over time, process innovations can shift labor to more skilled, higher wage jobs and widespread adoption of new capital equipment would enlarge downstream sectors of the economy.

In a recent survey of the literature, Vivarelli (2012) suggests that the microeconometric literature tends to support a positive link between innovation, proxied as R&D and/or product innovation, and employment; especially when focusing on high-tech sectors. The study also finds significant evidence in favor of the skill-biased hypothesis across different OECD countries, different economic sectors, and different types of innovation. For example, in one micro-econometric study Harrison, Jaumandreu, Mairesse, and Peters (2014) study the impact of innovation on employment using a comparable data set of firms from France, Germany, Spain, and the United Kingdom. The authors find that product innovation has a positive impact on employment, but that process innovation has a displacing effect on employment. However, the positive impact of product innovation generating employment is larger than the displacement effect of process innovation, and, therefore, the net effect of innovation on employment tends to be positive. Using a similar methodology, Hall, Lotti, and Mairesse (2008) find a low but positive effect of product innovation on employment in Italy, and no displacement effect from process innovation. Similar studies are Dachs and Peters (2014) for European countries 5
|||||requirement<br>function|for productivity<br>increases|
|---|---|---|---|---|---|
|ESMAP<br>(2011)|parametric|global|distribution<br>companies from<br>20 countries with<br>different system<br>sizes|fixed effects;<br>random effects|Introduction of<br>independent<br>regulator increased<br>labor productivity<br>by twice as high as<br>systems that have<br>introduced<br>regulation|
|Sanhueza,<br>Rudnick and<br>Lagunas<br>(2004)|non-<br>parametric|Chile|35 distribution<br>companies for the<br>year 2000|DEA|incorporating<br>distribution VAD<br>led to<br>improvements in<br>efficiency|
|Malik et al.<br>(2015)|parametric|India|unbalanced panel<br>of 385 electricity<br>generating units<br>for the years<br>1998-2009|panel data<br>econometric<br>based in fixed<br>effects|states unbundling<br>before the<br>Electricity Act of<br>2003 experienced<br>improvements in<br>operational<br>efficiency<br>especially after 3-5<br>years after<br>unbundling|
|Motta (2004)|parametric/<br>non-<br>parametric|Brazil|distribution<br>companies 1994<br>and 2000|DEA/Stochasti<br>c production<br>frontier|Privatisation has<br>no effect on<br>operating cost<br>efficiency, makes<br>case for including<br>capital costs in<br>benchmarking|
|Ramos-Real<br>et al. (2009)|non-<br>parametric|Brazil|panel of 18<br>distribution<br>companies from<br>1998-2005|DEA|incentives<br>generated in the<br>reforms process<br>incapable of<br>making firms<br>behave in more<br>efficient manner|
|Bonifaz and<br>Santin<br>(2000)|non-<br>parametric|Peru|panel of 19<br>distribution<br>operators 1995-<br>1998|DEA with 2<sup>nd</sup><br>stage<br>regressions|privatisation did<br>not lead to an<br>improvement in<br>terms of efficiency|
|Bonifaz and<br>Jaramillo<br>(2010)|parametric|Peru|panel of 19<br>distribution<br>companies for the<br>period 2000-2008|stochastic cost<br>frontier|private utilities are<br>less inefficient<br>than public<br>utilities due to<br>better management<br>practices|

28
# <u>Table A10. Reconciling mean and median regression results – first specification</u>

||**Paper re**|**gression**|**High Cook**|**’s distance**|**High standar**|**dized residuals**|**High dfb**|**eta - cog**|**High dfbe**|**ta - school**|
|---|---|---|---|---|---|---|---|---|---|---|
||_Median_<br>Monthly<br>|_Mean_<br>Monthly<br>|_Median_<br>Monthly<br>|_Mean_<br>Monthly<br>|_Median_<br>Monthly<br>|_Mean_<br>Monthly|_Median_<br>Monthly<br>|_Mean_<br>Monthly<br>|_Median_<br>Monthly<br>|_Mean_<br>Monthly<br>|
||earnings<br>(TC)|earnings<br>(TC)|earnings<br>(TC)|earnings<br>(TC)|earnings<br>(TC)|<br>earnings (TC)|earnings<br>(TC)|earnings<br>(TC)|earnings<br>(TC)|earnings<br>(TC)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Years of schooling (a1)|1.10*|2.88**|0.39|0.64|0.55|0.61|0.86|1.69*|1.08*|1.20|
||(0.60)|(1.15)|(0.58)|(0.76)|(0.52)|(0.77)|(0.62)|(0.93)|(0.63)|(0.79)|
|Cognitive skills (a2)|8.31***|6.28|8.13***|8.53***|7.87***|8.60***|9.00***|10.6***|7.81***|10.3***|
||(2.77)|(4.74)|(2.65)|(2.92)|(2.49)|(2.95)|(2.82)|(3.00)|(2.73)|(3.24)|
|SEM skills (a3)|16.5***|14.6***|17.1***|14.8***|17.3***|15.4***|17.2***|16.2***|17.3***|17.0***|
||(1.86)|(3.28)|(1.80)|(2.09)|(1.66)|(2.11)|(1.89)|(2.75)|(1.95)|(2.69)|
|Constant|42.1|56.3***|24.8|23.6|41.5|54.2***|24.5|62.5|18.8|60.8|
||(62.9)|(9.71)|(23.7)|(18.3)|(88.9)|(7.10)|(43.7)|(37.7)|(43.1)|(37.2)|
|Observations|1978|1978|1901|1901|1901|1901|1899|1899|1883|1883|
|R-squared|0.047|0.050|0.057|0.080|0.058|0.080|0.056|0.070|0.059|0.070|

* p<.10  ** p<.05  *** p<.01

_Notes_ . Columns (1) and (2) show mean and median regressions that correspond to columns (3) in Table 7 and Table A9. Columns (3) and (4) drop observations with Cook’s distance>4/N. Columns (5) and (6) drop observations with standardized residuals>2. Columns (7) and (8) drop observations with dfbeta>2/sqrt(N) for the cognitive skills coefficient. Columns (9) and (10) drop observations with dfbeta>2/sqrt(N) for the years of schooling coefficient.

90
**Table A - 2 : Number of Documents and Signatures Requirements for Importing, by Port of Entry**

|||<br>**Air Fre**|<br>**ight**|||<br>**Sea Fr**|<br>**eight**|||<br>**Road Tra**|<br>**nsport**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Selected PAFTA Countries**|**Document**|**s for Imprts**|**No. of S**|**gnatures**|**Document**|**s for Imprts**|**No. of S**|**ignatures**|**Document**|**s for Imprts**|**No. of**|**Signatures**|
||**Range**<sup>**(*)**</sup>|**Average**<sup>**(*)**</sup>|**Range**<sup>**(*) **</sup>|**Average**<sup>**(*)**</sup>|**Range**<sup>**(*)**</sup>|**Average**<sup>**(*)**</sup>|**Range**<sup>**(*)**</sup>|**Average**<sup>**(*)**</sup>|**Range**<sup>**(*)**</sup>|**Average**<sup>**(*)**</sup>|**Range**<sup>**(*)**</sup>|**Average**<sup>**(*)**</sup>|
|**Egypt**|5 - 7|6|5 - 10|8|6 - 8|7|6 - 12|9|4 - 8|6|6 - 11|10|
|**Jordan**|3 - 4|4|3 - 6|4|4 - 5|4|4 - 5|4|3 - 4|4|3 - 4|4|
|**Lebanon**|4 - 5|4|4 - 8|4|4 - 5|4|4 - 7|4|4 - 5|4|4 - 8|4|
|**Morocco**|4 - 7|5|3 - 7|4|5 - 7|6|5 - 9|8|4 - 5|4|3 - 5|3|
|**Saudi Arabia**|4 - 7|5|4 - 5|5|4 - 6|5|5 - 6|5|4 - 5|5|4 - 5|5|
|**Syria**|6 - 10|8|15 - 40|20|6 - 10|8|17 - 30|25|6 - 10|8|20 - 39|27|
|**Tunisia**|3 - 4|3|3 - 4|4|3 - 4|3|3 - 4|3|3 - 4|4|3 - 4|4|
|**UAE**|4 - 5|4|1 - 3|2|4 - 6|5|3 - 4|3|3 - 4|4|2 - 7|3|
|**Yemen**|8 - 9|8|20 - 21|20|8 - 9|8|7 - 24|20|5 - 8|8|7 - 22|20|
|**Total Countries (2008 Survey)**|**4 - 6**|**5**|**3 - 10**|**4**|**4 - 7**|**5**|**4 - 15**|**6**|**4 - 6**|**4**|**4 - 17**|**5**|
|**Total Countries (2001 Survey)**|**-**|**5**|**10 - 20**|**-**|**-**|**6**|**12 - 20**|**-**|**-**|**5**|**11 - 15**|**-**|

(*) See Footnote in Table A-1.

22
current production or the economic density of the selected districts. Table 14 demonstrates that if periodic maintenance is completely covered, between 8 and 19 percent of the district agricultural value added would be dedicated to roads maintenance and could then very rapidly reach an unsustainable point.

**Table 15: Share of the Potential Spending on Periodic Maintenance Covered by Agricultural Sales (per sq. km)**

||Periodic<br>maintenance<br>need (per<br>sq.km) (in<br>USD)|Share of the spending on<br>periodic maintenance<br>covered by agricultural<br>sales (per sq.km) (in<br>percentage)|
|---|---|---|
||**Tororo**<br>||
|Districtroads|357|**12%**|
|DR+communityroads|444|**15%**|
||**Bushenyi**<br>||
|Districtroads|241|**8%**|
|DR+communityroads|575|**19%**|

Note: Agricultural sales are computed based on production of the three main traded products multiplied by median selling prices and divided by district area. Figures are the following: USD 3,049 per sq. km in Tororo and USD 3,014 for Bushenyi.

Therefore, full routine maintenance and partial periodic maintenance should probably be ensured. But anyhow, expansion of the current district network (in our selected districts) should be avoided otherwise questions of unsustainability of the funding of the network will soon raise a concern.

# **5. What could a more effective road allocation maintenance be?**

The district roads maintenance fund in Uganda is allocated mainly by the length of the district road network in addition to a minimum standard amount for operational cost network (Ten year district, urban and community access roads investment plan, Ministry of Public Works and Transport, 2008). Therefore, the correlation between the actual and the “optimal” road maintenance fund allocation under different scenarios is calculated. The optimal allocation by district is a function of the agriculture potential, the 40
# A Field Experiment on the Role of Socioemotional Skills and Gender for Hiring in Turkey<sup>1</sup>

Efsan Nas Ozen, Stefan Hut, Victoria Levin and Ana Maria Munoz Boudet<sup>2</sup> JEL classification: J71, C93, J24, D63 Keywords: gender discrimination, socioemotional skills, labor market signaling, field experiments

> 1 This paper was produced with the support of a Trust Fund of the World Bank Umbrella Facility for Gender Equality. The grant supported a joint program of research on women’s economic participation in Turkey, implemented together between the World Bank and the Ministry of Family and Social Policy (Department for Women’s Economic Status). We would like to thank colleagues from the Ministry of Family and Social Policy and the (former) Ministry of Development of Turkey for their feedback an input at all stages of the research. We would also like to thank Maria Bigoni, Marco Casari, Juan-Camilo Cardenas, Margherita Fort, Alaka Holla, Macartan Humphreys, Bengisu Ozenc, Davide Raggi, Tanya Rosenblat, Eliana Rubiano, Giulio Zanella, and participants at the ESA World Meeting 2018 in Berlin, University of Bologna, IDEE Bologna and WESSI at NYUAD presentations for helpful comments and suggestions on previous versions of this paper. The team is thankful for Yonca Toker help in constructing the socioemotional skill signals, analyzing the reverse audit data, and coding the socioemotional skills for our dataset, and Meryem Dogan, Volkan Erdem, Burak Urun and Merve Dundar for their assistance. Almira Sekerci and Omer Faruk Zararsiz have also helped in collecting data for this project. The implementation of this project was done together with TEPAV (The Economic Policy Research Foundation of Turkey).

This study is registered in the AEA RCT Registry and the unique identifying number is AEARCTR-0002326. The usual disclaimer applies.

2 Authors affiliations: World Bank, Brown University, World Bank, World Bank. Corresponding author Efsan Nas Ozen (snasozen@worldbank.org)
respondent. Furthermore, most states require a similar number of documents to process an application.

# **_<u>5.3 Application Costs</u>_**

Some 21 percent of respondents felt it was “too costly” to apply for a driver’s license, while 14 percent said the same for land registration. About 11 percent of respondents were likely to report that the application process was “too costly” for government provided goods (such as government run schools or hospitals).

# **Figure 11: Cost of Application: Expensive**

Percent of adults that applied for a good or service in the past five years <!-- Start of picture text --> 25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Voter ID Aadhaar Card Driving License Register Utilities School Healthcare<br>Land/Property<br><!-- End of picture text --> <u>Note: This figure reports the percentage of respondents, from among those who applied for a good or service, who</u> found the application “too costly.” For example, 21 percent of those who applied for a driver’s license reported the application was too costly.

While the application cost may not appear to be very high, these results must be viewed in the context of exclusion: More than half the respondents could not even file an application, and the majority of those who could not apply cited a lack of affordability as the biggest reason. Further, given that these services are meant to be free, even a “reasonably priced” application fee could still create barriers for the very poor. Most respondents in Andhra Pradesh, Chhattisgarh, 36
Corridor takes twice as long as the Lusaka to Durban route. Port delays at Mombasa are 8 days longer than at the Port of Durban, while customs clearance processes in Uganda add an additional day to the total travel time.

_Rail service on the Uganda to Kenya route, which runs parallel to the Northern Corridor road, offers lower costs but delays can be significant._ The Rift Valley Railway Company connects landlocked Uganda to the port of Mombasa providing an alternative to road freight along the Northern Corridor. A comparison of this parallel road and rail corridors suggests that freight tariffs on the northern corridor are between $0.07 and $0.09 per tonne-kilometer, slightly more than the Kenya-Uganda railway tariff ($0.05 cents per tonne-kilometer). Nevertheless, the railway often encounters additional delays due to disrepair or other service disruptions..

# Roads

## **Achievements**

_Road density in Uganda is rather high, with relatively high volumes of traffic_ . The classified road density at 365 km per 1000 km<sup>2</sup> is four times the density of an average low-income African country. The paved roads carry twice as much traffic as those of low-income African peers, and indeed as much as Africa’s middle-income countries (MICs) (table 4).

## **Table 4. Benchmarking** **<u>Uganda’s roads, 2005</u>**

|Indicator|Unit|Low-income,<br>nonfragile<br>countries|**Uganda**|Middle-<br>income<br>countries|
|---|---|---|---|---|
|Classified road network density|km/1,000 km<sup>2</sup>of land area|88|**360**|278|
|Total road network density [1]|km/1,000 km<sup>2</sup>of land area|132|**385**|318|
|GIS Rural accessibility|% of ruralpopwithin 2 km from all-season road|25|**26**|31|
|Main road network condition[2]|% ingood or fair condition|72|**87**|86|
|Rural road network condition[3]|% ingood or fair condition|53|**39**|65|
|Classifiedpaved road traffic|AADT|1,131|**2,460**|2,451|
|Classified unpaved road traffic|AADT|57|**54**|107|
|Primarynetwork overengineering|% ofprimarynetworkpaved with 300 AADT or less|30|**12**|18|
|Primarynetwork underengineering|% ofprimarynetwork unpaved with 300 AADT or more|13|**26**|20|
|Perceived transport quality [4]|% firms identifying transport as major business<br>constraint|28|**22**|18|

_Source:_ AICD Road Sector Database on 40 Sub-Saharan African countries. _Note:_

[1] Total network includes the classified and estimates of unclassified and urban networks.

[2] Main network for most countries is defined as result of adding the primary and secondary networks. Uganda is only primary.

[3] Rural network is generally defined as the tertiary network and does not include the unclassified roads.

- <u>[4] Source: World Bank—IFC Enterprise Surveys on 32 Sub-Saharan African countries.</u>

18
where _φt_ and _<u>ψt</u>_ are unweighted average productivity and market share, respectively. The first component, Φ<sup>_U_</sup> _t_<sup>,isunweightedaverageproductivity.Thesecondcomponent,</sup><sup>_Rt_,measuresthe</sup> covariance between firm productivity and market share; changes in this measure represent a reallocation of market share between firms of different productivity levels. The “new new” trade theory suggests that trade liberalization should cause the reallocation component to rise, as output is reallocated from less productive to more productive firms. In addition, it is important to note that using this decomposition, the average productivity component can change not only due to changes in productivity among existing firms, but also due to firm entry or exit. Therefore, recent trade theory suggests that the average productivity component is also likely to increase as the least productive firms exit in response to competition from trade.

We first construct these measures at the all-India level. To make the results representative of the population of firms, and consistent over time, we pre-multiply each observation by the sampling weight provided in the ASI. Furthermore, to make the results more comparable with our later regression results, we consider only firms in state-industry groups that exist over the entire period.<sup>11</sup> Figure 2 and Table 4 present results. Following Pavcnik (2002), we normalize productivity values to be zero in 1985, so changes in productivity levels can be interpreted as growth since 1985. Between 1985 and 2004, aggregate productivity grew by 19%. This increase in productivity implies an annual increase of slightly less than 1% per year, within the range of previous studies.<sup>12</sup> When we consider the time period as a whole, nearly all of this increase (17.1%) can be attributed to growth in average productivity, rather than reallocation. However, Figure 2 and Table 4 suggest that there are three distinct phases between 1985 and 2004. First, from 1985 to 1990, average productivity rose by over 8%, while the reallocation component actually fell by more than 6%, indicating that more productive firms lost market share to less productive firms. Starting in 1991,

> 11We have confirmed that including all firms makes little difference to the overall results.

> 12There has been an extensive debate about TFP growth in the organized Indian manufacturing sector, particularly during the 1980’s; Goldar (December 7, 2002) provides a summary of a number of TFP growth estimates, and discusses many of the measurement issues involved. It is important to note that our TFP estimates are based on a gross output, rather than value-added, production function; value-added TFP growth rates tend to be much higher than gross output growth rates.

15