rafmacalaba/datause-encoder-data huggingface dataset:
Among papers that analyze recent changes in the labor market conditions in post-communist countries, <u>Privalko (2020) evaluates the effects of age, gender, and tenure on the labor market</u> trajectories of Russian workers, finding a negative correlation between tenure and job mobility. Grotkowska, Wincenciak, and Gajderowicz (2018) estimate the long-term public sector wage premium in Poland, using job tenure as a measure of human capital. They document a positive selection into both public and private employment, even though employees earned more in the public sector, which might be an indication of efficient job markets. Generally, the more job experience a worker had, the smaller the public sector wage premium was for each additional year of tenure. Kupets (2015) studies the efficiency of job matching in Armenia, Georgia, Macedonia, and Ukraine in 2012–13. He shows that many workers with long tenure appear to be overeducated, implying that workers with long tenure may be trapped in jobs with low educational requirements because of the high tenure premium in post-communist countries.

# **3. Data and descriptive statistics**

The data from this study come primarily from labor force surveys of Albania, Bosnia and Herzegovina, Kosovo, Moldova, North Macedonia, <mark>Türkiye</mark> , and Serbia and from the Russian Longitudinal Mobility Survey (RLMS). The pooled data set consists of 4.4 million individuallevel observations across 7 countries and 26 years (1994–2020). Of these observations, about 2.3 million contain information on job tenure. Table 1 summarizes the data coverage for the sample with full data on job tenure.

To achieve intertemporal and cross-country comparability, we harmonized variables across yearly vintages of labor surveys. The years of coverage differ by country, with data from Russia spanning 25 years (1994–2020, with gaps) and data for Moldova covering just 7 years (2003–09). The period 2008–18 has the most extensive country coverage. Our analysis focuses on the working-age population (people 20–65).

The main variable of analysis—the number of years in the current job or job tenure—is available for Albania, Bosnia and Herzegovina, Kosovo, North Macedonia, Russia, <mark>Türkiye,</mark> and Serbia. For Moldova, the tenure variable is available in categories, which we classified as short (less than 1 year), medium (5–10 years), and long (more than 10 years). The data set also contains variables 6
with consumption shares _θi_ .

Given the Cobb-Douglas preferences, the domestic consumption of sector _i_ product, _c_<sup>_i_</sup> _t_<sup>,canbe</sup> expressed as where the income _Mt_ equals _Mt_ =<sup>�</sup> _i_<sup>_p_</sup> _t_<sup>_iq_</sup> _t_<sup>_i_.</sup> We pin down prices by setting _qt_<sup>_i_=</sup><sup>_ci_</sup> _t_<sup>inthenon-tradablesectors9.Inthetradedsectors,the</sup> net exports are equal to It is useful to write the net exports relative to time _t_ 0 variables for calibration purposes: where _q_ ̃ _t_<sup>_i_</sup> 0<sup>and ̃</sup><sup>_c_</sup> _t_<sup>_i_</sup> 0<sup>are production and consumption of product</sup><sup>_i_, and they can be directly plugged</sup> in from the data<sup>10</sup> .

> 9In this paper, the non-tradable sectors are: construction, retail and other services.

> 10Particularly from Input-Output tables

17
Table 4: Table Fixed E¤ects, Full Sample

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Language<br>out of 24|Math<br>out of 24|Language<br>out of 24|Math<br>out of 24|
|Years of exposure to FTS|0.12<br>(0.06)*|0.21<br>(0.07)***|0.08<br>(0.07)|0.16<br>(0.07)**|
|Pupil is female =1|1.04|0.11|1.15|0.16|
||(0.06)***|(0.06)*|(0.08)***|(0.08)**|
|Nr of people living in pupil’s household|-0.13|-0.08|-0.13|-0.07|
||(0.02)***|(0.02)***|(0.02)***|(0.02)***|
|Nr of rooms in pupil’s home|0.11|0.10|0.06|0.04|
||(0.02)***|(0.02)***|(0.03)*|(0.03)|
|Home equipm. modern appliances PCF1|0.52|0.58|0.48|0.52|
||(0.04)***|(0.04)***|(0.05)***|(0.05)***|
|Mother completed primary education|0.33<br>(0.11)***|0.33<br>(0.11)***|0.35<br>(0.12)***|1.00<br>(0.11)***|
|Mother has some secondary education|0.47<br>(0.08)***|0.46<br>(0.08)***|0.45<br>(0.10)***|0.49<br>(0.10)***|
|Mother completed secondary education|0.63<br>(0.09)***|0.62<br>(0.09)***|0.60<br>(0.13)***|0.49<br>(0.13)***|
|Mother completed tertiary education|0.53<br>(0.12)***|0.63<br>(0.12)***|0.52<br>(0.21)**|0.59<br>(0.21)***|
|Teacher Experience 1 year or less =1|-0.83<br>(0.22)***|-1.33<br>(0.23)***|-0.89<br>(0.25)***|-1.13<br>(0.25)***|
|Teacher Experience in years|0.00|0.03|0.02|0.05|
||(0.01)|(0.01)***|(0.01)***|(0.01)***|
|Class size|0.01|0.02|0.01|0.00|
||(0.01)|(0.01)**|(0.01)|(0.01)|
|This is an observation in 2002 =1|0.78|0.68|0.74|0.82|
||(0.10)***|(0.10)***|(0.12)***|(0.12)***|
|Constant|13.12|10.59|12.46|10.38|
||(0.28)***|(0.29)***|(0.35)***|(0.35)***|
|Observations|17249|17360|11061|11115|
|Number of Unique school identi...ers|218|218|152|152|
|R-squared|0.08|0.08|0.08|0.08|
|Socioecon. context quintiles in sample|All|All|3, 4 and 5|3, 4 and 5|
|Number of full time schools|31|31|31|31|
|Number of comparison schools|187|187|121|121|

Notes: The dependent variable is the raw test score on a scale of 0 to 20. Columns 1 and 2 include all 218 schools for which information was available in both 1996 and 2002, and who were not full time at baseline. Columns 3 and 4 are restricted to those of the 218 schools that pertain to the 3 least favored socio-economic quintiles. Those quintiles were calculated by ANEP in basis of the information that was collected in 1996. None of the schools pertaining to socio-economic quintiles 1 and 2 ever participated in the full time program. Standard errors are reported in parentheses. * signi...cant at 10 percent; ** signi...cant at 5 percent; *** signi...cant at 1 percent.

13
# **_Q. What skills are specified in job descriptions? Do these skill demands vary between industry, occupation, or gender of jobseekers?_**

# **_A. Programming skills are most frequently required (63% of job postings), followed by sales skills (12%). There are differences in skill requirements between occupations and genders. For example, finance skills are most frequently demanded for managers, while communication skills are more frequently demanded for intern- and entry-level positions. By gender, communication and Microsoft skills are more frequently required for female workers._**

The 10 most frequently required skill categories are programming, sales, designing, communication, finance, Microsoft, analytics and research, other soft skills,<sup>18</sup> leadership and management and others (Figure 13). Programming skills are required by 63% of all job postings, followed by sales skills (12%), designing skills (6%), and communication skills (4%).

**Figure 13. Required skills**

<!-- Start of picture text --> 3% 3%2%2%1%<br>4%<br>4%<br>6%<br>12% 63%<br>Programming Sales Designing<br>Communication Finance Microsoft<br>Analytical/ Research Other soft skills Leadership/ Management<br>Others<br><!-- End of picture text --> _Source_ : Authors using the data from Rozee.pk.

Figure 14 shows required skills by industries. Programming skills are most frequently required in ICT jobs (82%). Interestingly they are also required in other industries (e.g., 44% in professional, scientific, and technical jobs; 37% in administrative, social, and personal service jobs; and 26% in education and health jobs). This high demand for programming skills in all industries may indicate that employers tend to use online job portals when they look for programming-related skills. Sales’ skills are also frequently required in all industries except ICT.

> 18 These include skills such as: time management, team player, multitasking, emergency handling, teamwork.

13
16 surviving and new firms. In doing so, we attempt to understand the dynamics of investment to derive a reasonable growth rate that can be applied in the construction of capital stocks for the manufacturing sector.

Surviving firms accounted for 65% of total flows of investment in 2010, while new firms accounted for the remaining 35%. Young firms that entered the census frame accounted for about 40% of total investment of new firms in 2010, while the remaining 60% is comprised of firms that entered through the expanded coverage of active firms in the census (figure 10, panel a). From the profile of surviving firms, old firms contributed 63% of gross investment flows. Investments of new and surviving firms increased by an average rate of 11% and 3.6%, respectively, during 2005–10. However, overall investments of firms grew at a rate close to zero during 2000–10, thereby providing guidance for our assumption, g. In other words, the average investment growth rate within a firm, g , is found to be constant across the three census periods.

However, the main limitation in deriving g from these census periods is that it effectively excludes investments by firms during the noncensus periods. This could potentially contribute to mismeasurement of the “actual” g because the overall investment rate in the manufacturing sector is much larger than the g that we derived from the census data (2005, 3.2%; 2010, 18.4%).

**Figure 10:** Investment of New and Surviving Firms<sup>a</sup>

a. Investment (percent) b. Average Investment (RM thousand) <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Percent of total investment<br><!-- End of picture text --> <!-- Start of picture text --> 25.7<br>49.5 44.8<br>63.1<br><!-- End of picture text --> <!-- Start of picture text --> Young<br><!-- End of picture text --> <!-- Start of picture text --> RM thousand<br><!-- End of picture text --> <!-- Start of picture text --> Young<br><!-- End of picture text --> Source: Note: Young firms are ≤6 years in operations; middle-aged firms are 6–15 years in operations; old firms are ≥16 years in operations. Ave I are average investments of young, middle-aged, and old firms respectively for every census period. a. Firms that reported an inconsistent year of starting operations over the three census periods are removed from the analysis of new and surviving firms. Approximately 9.5% of the total number of firms was removed.

Based on our analysis, the average investments of surviving firms were between two and four times higher than that of new firms during the 2005 and 2010 census periods. This provides support for our hypothesis that a large proportion of the book values reported by firms in the census may be “undervalued” because surviving firms that were middle-aged and old were the main capital accumulators. Net investments, measured as gross investments after accounting for depreciation and disposals, were slightly positive for census years 2000 and 2010 but negative for census year 2005 (figure 11).
# **Do Institutions Matter for FDI Spillovers?**

**The Implications of China’s “Special Characteristics”**

Luosha Du<sup>1</sup> , UC Berkeley Ann Harrison<sup>2</sup> , World Bank, UC Berkeley and National Bureau of Economic Research Gary Jefferson<sup>3</sup> , Brandeis University

> 1 Ph.D. candidate, Department of Agricultural and Resource Economics, University of California, Berkeley, 310 Giannini Hall, #3310, Berkeley, 94720-3310. Email: luosha@berkeley.edu.  All three authors gratefully acknowledge the conference organizers at Fudan University, particularly to Peter Petri. We would also like to thank Justin Lin, Max Auffhammer, and Jeremy Magruder, for their valuable comments, as well as seminar participants at Yale University, the Wharton Management Department at the University of Pennsylvania, and the World Bank. This work is partially based on work supported by the National Science Foundation under grant No. 0519902, and through a grant from the Research Support Budget of the World Bank.

> 2 World Bank, 1818 H Street, N.W. 20433 Email: aharrison2@worldbank.org

> 3 Brandeis University, P.O. Box 9110 Waltham, MA, 02454 Email: Jefferson@brandeis,edu.
# **Table A14. Associations between SCP Quality Determinants and Labor Market Outcomes** **_Using Individual- and Program-Level Data for Ecuador_**

||**Ever**<br>**employed the**<br>**year after**<br>**graduation**|**Percent of time**<br>**employed after**<br>**graduation**|**Average**<br>**monthly**<br>**wage (log)**|
|---|---|---|---|
||<br>(1)|(2)|(3)|
|_Infrastructure_||||
|Number of workshops or labs available for practice||2.645***<br>(0.445)||
|_Costs_||||
|Government scholarships are used by some students||7.623*<br>(4.241)||
|_Training and curriculum_||||
|Analyze student performance to solve problems more than once||-8.775**||
|<br>per year||(3.818)||
||0.137***|7.221*|0.650**|
|Collect student satisfaction data more than once per year|<br>(0.0446)|<br>(3.678)|<br>(0.277)|
|Government standards are very important to update program|0.0546<br>|21.14***<br>|0.611**<br>|
||(0.0402)|(3.658)|(0.246)|
|Enrollment trends are very important to update program||-5.478<br>(4.142)||
|Student feedback is very important to update program||10.65***<br>(3.310)||
||0.209***|<br>17.42***|1.212***|
|Teaches numerical competencies|(0.0566)|(6.273)|(0.357)|
||-0.0850*|||
|Teaches a foreign language|(0.0434)|||
||-0100**||-0682**|
|Professional association test required for graduation|.<br>(0.0443)||.<br>(0.285)|
|Time assigned to practical training (%)||-0.253**<br>||
|||(0.122)||
|Offers remediation classes before starting the program||-6.298<br>(3.821)||
|_Link with productive sector_||||
|Industry lends or provides equipment to program for student training||11.11***<br>(3.309)||
|HEI ti tdt f b iti|0.217***|<br>16.11**|1.491***|
|rans suens or jo nervews|(0.0522)|(6.982)|(0.347)|
|HEI coordinates job interviews with firms||-11.31***<br>(4048)||
||0.107**|.<br>21.23***|0.537*|
|HEI provides job market information|(0.0425)|(3.710)|(0.297)|
|HEI has agreements with private firms to hire graduates||6.911**<br>(3.341)||
||0159***|<br>7536|1131***|
|Somebody (from board or staff) is in charge of industry relations|.<br>(0.0604)|.<br>(4.980)|.<br>(0.429)|
|_Faculty_||||
|Classroom observation is very important in faculty evaluation||7.454*<br>(4.460)||
|Students and peers' informal comments are very important in faculty||<br>-9.928**||
|<br>evaluation||(4.793)||
|Research skills are very important when hiring faculty|-0.171***<br>(00488)|<br>-26.70***<br>(4485)|-1.331***<br>(0275)|
||.<br>-0.0628|.<br>-3.187|.<br>-0.363|
|Number of faculty (log)|(0.0379)|(2.836)|(0.261)|
|||<br>0.177***||
|Percent of faculty with 5yrs+ industry experience||(00431)||
||-0.00122*|.<br>-0.110**|-0.00887**|
|Percent of faculty working in the industry|(0.000631)|(0.0514)|(0.00423)|
|Percent of faculty with SCP degree||-0.212***<br>(0.0680)||
|Percent of <40 years old faculty||-0.218***<br>(0.0785)||

90
# _Rainfall:_

Our rainfall data come from the GPCP<sup>3</sup> database, which provides rainfall estimates from 1979 to present day, published by the precipitation research group at NASA. This rainfall measure has been used in the literature several times, notably in Miguel (2004) and Bruckner and Ciccone (2011). GPCP provides monthly averages of rainfall in 2.5 degree latitude and longitude intervals. These monthly averages are then multiplied by the number of days in each month to produce an estimate of monthly rainfall. These monthly rainfall estimates are then summed to get an annual rainfall estimate. Since 2.5 degree latitude and longitude intervals do not represent countries accurately, we follow the approach of Miguel (2004) to aggregate rainfall in these intervals to country levels in SSA. The GPCP data set utilizes weather station measures in conjunction with satellite observations of cold cloud cover.

# _Commodity index:_

We assemble a country-specific commodity export price index following the model: where _ComPricec,t_ is the international price of commodity c in year t, and _θi,c_ is the average (time invariant) value of exports of commodity _c_ in the GDP of country _i._ Our commodity data comes from two sources. The first of these is the NBER-UN trade data, which we use for export values. Real commodity price index come from the UNCTAD commodity statistics. We included aluminum, beef, coffee, cocoa, copper, cotton, gold, iron, maize, oil, rice, rubber, sugar, tea, tobacco, wheat, and wood in our index, following Arezki and Bruckner (2014).

# **III.2 Capital Flows to SSA**

Figures 1 and 2 present an overview about total capital inflows to the 38 Sub-Saharan Africa countries, in terms of 38 SSA countries’ total GDP (Figure 1, shown in section 1) and current US dollars (Figure 2),

> 3 GPCP rainfall data is publicly available from http://precip.gsfc.nasa.gov/

8
**Table 2: Tanzanian Maize and Other Coarse Grains Trade Patterns (2001)**

|**Region**|**Export Share**<br>**(%)**|**Import Share**<br>**(%)**|**Exports**<br>**(USD Millions)**|**Imports**<br>**(USD Millions)**|
|---|---|---|---|---|
||**I**|**II**|**III**|**IV**|
|Argentina|0.79|0.00|0.1246|0.0000|
|Brazil|0.60|0.00|0.0948|0.0001|
|China|2.01|0.05|0.3177|0.0028|
|East Asia|10.69|0.10|1.6896|0.0051|
|Eastern Europe and Former<br>USSR|3.80|0.16|0.6000|0.0082|
|Latin America and the<br>Caribbean|1.53|4.47|0.2419|0.2369|
|Malawi|0.05|8.02|0.0083|0.4255|
|Mexico|1.08|0.15|0.1708|0.0079|
|Middle East & North Africa|2.67|1.37|0.4221|0.0725|
|Mozambique|0.68|0.01|0.1068|0.0007|
|Oceania|1.39|0.01|0.2196|0.0004|
|Rest of East Africa|0.05|0.09|0.0085|0.0046|
|Rest of North America|2.22|0.00|0.3515|0.0001|
|Rest of Sub-Saharan Africa|19.50|4.56|3.0813|0.2418|
|South Asia|0.54|0.23|0.0849|0.012|
|Southern Africa|0.58|0.92|0.0914|0.0488|
|Uganda|0.10|7.20|0.0165|0.3818|
|USA|14.89|34.02|2.3528|1.8045|
|Western Europe|36.51|34.02|5.7693|1.8047|
|Zambia|0.32|2.04|0.0500|0.1084|
|Zimbabwe|0.01|2.59|0.0015|0.1375|
|Total|100|100|15.8039|5.3043|

Source: Dimaranan (2006) In principle, Tanzania should be affected by climate volatility in major grain exporting or importing countries since that volatility translates into maize production volatility, subsequently affecting the import price, the world price, and the demand for Tanzanian maize. Trade may thus allow Tanzania the opportunity to take advantage of international climate and production heterogeneity in any given year to either mitigate the impacts of a lower-than-usual yield or take advantage of a higherthan-usual yield. For example, if Tanzanian maize productivity is above trend in a year when a major global maize trader like the USA has below-trend productivity, Tanzania may be able to benefit by exporting more maize at higher world prices.

This point is best illustrated by Table 3 (column I), describing the international correlations in maize production deviations from trend between 1971 and 2001 for Tanzania and the world’s major 6
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# **<mark>The Macroeconomic Impact of Climate Shocks in Uruguay</mark>**

# Fernando Giuliano, Daniel Navia, Heather Ruberl<sup>§</sup>

**Keywords** : Uruguay; macro-structural model; climate shocks; droughts; floods; climate change.

**<mark>JEL Classification</mark>** <mark>: C10, C50, E37, Q54</mark>

§ World Bank; Macroeconomics, Trade and Investment Global Practice. This paper was produced under the guidance of Doerte Doemeland. The short-term distributional analysis was jointly developed with Lourdes Rodriguez Chamussy and Diego Tuzman Fernandez from the Poverty and Equity Global Practice. The team would like to thank Marianne Fay, Dirk Heine, Mariana Conte Grand, Ana Bucher, Lucia Spinelli, Florencia Balestro, Carlos Costa, Julieta Schiro, Andrew Burns, Charl Jooste, Edward Bresnyan, Pedro Valdivia, and Florent McIsaac, who provided valuable feedback and advice at different stages of the study, and Marielena Perez for her research assistance. This study benefited from a close collaboration with Uruguayan technical teams at the Ministry of Finance and the Ministry of Livestock, Agriculture, and Fishing. The team would like to thank the following current and former government officials which helped improve the study, none of whom should be held accountable for any mistakes or misinterpretations. At the Ministry of Finance (in alphabetical order): Marcelo Caffera, Juan Chaves, Lucia Heguy, Juan Labat, Gabriela Miraballes, Victoria Novas, Maria Luisa Olivera, Nicole Perelmuter, Maria Tornaria, Carolina Steneri. At the Ministry of Livestock, Agriculture, and Fishing: Ángela Cortelezzi and Veronica Duran.

1
# **Table A9:  European Bank for Reconstruction and Development Transition Report (EBR)**

**Data Provider** European Bank for Reconstruction and Development **Description** Multilateral development bank based in London, United Kingdom **Website** www.ebrd.org **Data Source** Transition Report **Type** Expert Assessment **Respondents** EBRD staff **Frequency** Annual since 1996 **Coverage** Transition economies **Public Access** Yes **Description** The Transition Report includes scores on a 5-point scale for eight Transition Indicators measuring progress towards market economy status.  Scores are based on a checklist of underlying criteria and reflect the views of EBRD staff.

|**Voice and Accountability**|2008|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|---|---|
|NA|..|..|..|..|..|..|..|..|..|..|
|**Political Stability and Absence of Violence**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Government Effectiveness**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Regulatory Quality**|||||||||||
|Price liberalisation|X|X|X|X|X|X|X|X|X|X|
|Trade & foreign exchange system|X|X|X|X|X|X|X|X|X|X|
|Competition policy|X|X|X|X|X|X|X|X|X|X|
|**Rule of Law**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Control of Corruption**<br>NA|..|..|..|..|..|..|..|..|..|..|
|**Country coverage**|**29**|**29**|**29**|**27**|**27**|**27**|**27**|**26**|**26**|**27**|
|**Year of Publication**|**2008**|**2007**|**2006**|**2005**|**2004**|**2003**|**2002**|**2000**|**1998**|**1996**|

48