rafmacalaba/datause-encoder-data huggingface dataset:
||**Table 5: Effects of in**<br>Occupational skill tercile:|**ternet acce**<br>F|**ss on municipa**<br>irst|**lity-level wo**<br>Se|**rker outcomes,**<br>cond|**by skill lev**<br>T|**el**<br>hird|
|---|---|---|---|---|---|---|---|
|**Row**|**Outcome**|**Internet**<br>**Coef.**|**lag Internet**<br>**Coef.**|<br>**Internet**<br>**Coef.**|**lag Internet**<br>**Coef.**|**Internet**<br>**Coef.**|**lag Internet**<br>**Coef.**|
|(1)|Total new hires|0.002|-0.089***|0.014|-0.011|-0.065***|-0.111***|
|(2)|Total separations|0.001|-0.076***|-0.001|0.007|-0.047**|-0.058***|
|(3)|Employment|-0.019**|-0.073***|0.014|0.012|-0.024**|0.006|
|(4)|Average monthly wage|-0.001|0.000|-0.001|-0.002|-0.025***|-0.038***|
|(5)|SD (monthly wage)|-0.017|-0.051***|0.002|-0.041**|-0.013|-0.057***|
|(6)|90-10 difference in ln(wage)|0.018**|0.008|0.027*|-0.017|-0.020*|-0.078***|
|(7)|75-25 difference in ln(wage)|0.007*|0.003|0.006|-0.010|-0.007|-0.048***|

Note: Estimates based on RAIS employee-employer records aggregated by municipality and year merged to internet access data from the School Census. Outcome measures listed in rows are in natural log. Interpretation of coefficients relate an increase in internet access from 0% to 100% in the municipality to the percent change in the outcome measure. Rows indicate outcome used in a common specification including unreported vectors of municipality and year fixed effects and a constant term. Standard errors clustered by municipality in parentheses. * p<.1, ** p<.05, *** p<.01.

We then estimate similar employment and wage coefficients by sector and skill level, shown in Table 6. (Note that not all sectors employ workers in each tercile, which leads to only some sector-skill cells being estimated.) The reduction in employment in wholesale and retail trade occurs largely among low-skill occupations, whereas the opposite is true among public administration (which is comprised of only highskill occupations). The manufacturing sector sees gains in middle and higher skill occupations: on average across municipalities, the two-year cumulative effect of an increase in internet access (from 0 to 100%) on employment is 8.8% and 13.5% for medium- and high-skill occupations in manufacturing industries, respectively.

13
In Table 4, we show the lower and upper estimate outcomes for capital‐to‐output ratios when we consider (i) lower population growth and the associated human capital growth for this population trend; and (ii) higher on average investment‐to‐output ratios and a higher LFPR. Other variables remain unchanged from their central paths in the simulations. For both the lower and upper estimate scenarios, the K/Y ratios in 2018 are slightly higher than in the central projections but remain lower than the 2010 levels.

**<u>Table 4: Simulation of Syria’s Conflict Years (2011‐2018)</u>**

**– Lower and Upper Estimates and Impact on Capital‐to‐Output Ratios**

|**Variable**|**Note**|**Average**|**2018**|
|---|---|---|---|
|||**2011‐18**||
|**A. Lower Estima**|**te**<br>|||
|Population growthg�|_(1)_|‐0.030|0.000|
|Human capital growthg�|_(2)_|_‐_0.019|‐0.006|
|<br>_Capital‐to‐Output (K/Y) Ratios_<br>|_(with D_|_amage)_||
|SimulatedK�<sup>�</sup>/Y||1.180|1.091|
|SimulatedK<sup>�</sup>/Y||1.175|0.737|
|�<br>**B. Upper Estima**<br>|**te**<br>|||
|Public investment‐to‐output ratioI<sup>�</sup>/Y|_(3)_|0.070|0.070|
|Private investment‐to‐output ratioI<sup>�</sup>/Y|_(3)_|0.090|0.090|
|Labor force participation rate, growth,g|_(4)_|0.007|0.006|
|ρ<br>_Capital‐to‐Output (K/Y) Ratios_<br>|<br>_(with D_|_amage)_||
|SimulatedK�<sup>�</sup>/Y||1.133|1.127|
|SimulatedK<sup>�</sup>/Y||1.139|0.784|
|�<br>*Multiply by 100 to obtain parameter/variable v<br>growth terms (%).|alues in p|ercentage sh|are or|

- _(1)_ Based on a further downward adjustment to the UN population statistics to reflect higher migration and fatalities than the central projection. See Section 2.2. Population changes over 2011‐2018 are calibrated such that they follow the trend in the UN population statistics but with steeper slopes.

- _(2)_ Human capital growth is calculated based on the adjusted population data. See Section 3.1.3. As in the central projection, we keep the index of human capital unchanged from 2010 to 2012, such that the contraction mainly occurs over 2013‐2015 with average growth of ‐4.37% (average growth over 2016‐2018: 0.57%).

- _(3)_ Investment‐to‐output ratios are based on ESCWA (2018) estimates. See Section 3.1.1.

- _(4)_ Calculated based on the growth in LFPRs for men and women above 15 years of age over 2010‐2018 reported in World Bank (2019). Female and male shares of the working‐age population follow United Nations (2017).

Graphs 9(a) ‐ 9(b) show the outcomes of simulations for GDP level and growth over the conflict years given the calibrations in Table 3 and Table 4. Our simulations across the three scenarios (central, lower and upper estimate projections) indicate that the depletion of factors of production alone may account for about 87 percent of the negative GDP growth on average, and further, that about 64 percent of the average negative growth is due to physical capital destruction. Demographics and labor 20
To date, however, there is no indication that Latvia has succeeded in moving beyond this first step. More than 3⁄4 of total FDI was concentrated in low and medium-low technology industries. And according to a recent FIAS analysis, knowledge intensive sectors, excluding telecommunications, only attracted 3% of the total FDI flows.

Even though the FDI grew steadily in recent years, as the table below indicates, there is reason to believe that it generated limited knowledge transfers to the Latvian business community in terms of know-how, technology, or access of Latvian firms to global markets.

**Table 2 Foreign Investment Stock by Sector of Economy**

|_Sector_|_1997_|_1998_|_1999_|_2000_|_Total 1997-2000_|
|---|---|---|---|---|---|
|||_millions o_|_f dollars_||_% of total_|
|Transport, storage and<br>communications|309.5|346.8|331.7|475.2|26.5%|
|Financial Intermediation|179.3|264.9|252.1|429|20.3%|
|Manufacturing|235.3|199.1|228.6|401.5|19.2%|
|Wholesale and retail trade,<br>maintenance and repairs|74|185.2|217|349|14.9%|
|Real estate, leasing, R&D, and other<br>commercial activities|18.4|50.4|98.5|203.3|6.7%|
|Other investment|67.6|55.5|119.7|30.1|4.9%|
|Electricity, gas and water supply|15.3|20.8|34.8|106.8|3.2%|
|Hotels and restaurants|13.2|14.7|18.5|36.6|1.5%|
|Agriculture, hunting, forestry, fishing|1.1|8.9|9.6|26.7|0.8%|
|Health and social Work|7.6|7|6.7|14.7|0.7%|
|Mining and quarrying|1.2|2.8|6.5|15.1|0.5%|
|Construction|5.1|3.6|4.5|6.3|0.4%|
|Other social and individual services|1.6|2.5|4.1|8.5|0.3%|
|Education|0.9|0.9|1|1.3|0.1%|
|Total|901.4|1162.6|1333.3|2,104.60|100.0%|
|_Source:_U.S. Department of Trade, 200<br>LATVIA2002-CH-7:-004D51AF<br>|3, www.us|atrade.gov/|Website/CC|G.nsf/CCG|url/CCG-|

Furthermore, as the chart below indicates, Latvian manufacturing companies have had only a limited success to in attracting foreign investors to knowledge intensive industries. While FDI in low-technology and medium-low technology industries grew rapidly, the share of FDI in knowledge-based industries remained insignificant -- less than 0.8% in 1999-2001.

21
dividual income components. At the aggregate level, the survey asked households: “What is the total monthly income of your household at the moment?” In addition to the single question on monthly household income, the survey also collected individual income components, including the WWS stipend, through different modules. We define Income-1 as the monthly income as reported by the household and Income-2 as monthly income derived through summation of different income components including WWS stipend. Although the survey collected information on the WWS stipend, it did not collect information on the duration for the WWS stipend payments.<sup>17</sup> Nevertheless, WWS participants received LVL 100 per month and there is no evidence suggesting under or over payments, we added the LVL 100 (LVL 100 for T1; 0 for household in C1) to household monthly income to get total monthly household income including WWS stipend.<sup>18</sup> There are marginal differences in average incomes based on the two income definitions (reported in Table 1). As program impacts differ based on income choice, we report impacts using both income measures. However, our preferred income measure is income-2, because it is obtained by aggregating different income components, and households are more likely to omit the WWS stipend in reporting the total aggregate monthly income.

# **3 Methodology**

## **3.1 Targeting Performance**

To assess WWS program targeting performance, we rank beneficiary households in the welfare distribution of the entire population. However, by design, the WWS Household Survey used in this paper represents _only_ the registered unemployed population; the ranking of

> 17It is also not feasible to find out the duration of payment from the time spent in WWS program, as the duration of payment will not exactly match the time spent in the program.

> 18Almost 99 percent of assigned individuals in group T1 reported that payments were correct. As discussed in the data section, only one person (the assigned person) in each household is participating in WWS in the T1 group, and no one in the C1 group is in the WWS program.

9
Table 3a: Employment share in unorganized sector by manufacturing industry

|NIC|IndustryDescription|1989|1994|2000|2005|
|---|---|---|---|---|---|
|15|Food products and beverages|0.85|0.83|0.84|0.82|
|16|Tobacco products|0.87|0.81|0.87|0.90|
|17|Textiles|0.82|0.82|0.82|0.82|
|18|Wearing apparel; dressing and dyeing of fur|0.79|0.68|0.94|0.92|
|19|Tanning and dressing of leather; luggage, handbags, saddlery, harness and footwear|0.85|0.80|0.76|0.75|
|20|Wood and wood products, except furniture; articles of straw and plating material|0.99|0.99|0.99|0.98|
|21|Paper and paper products|0.52|0.52|0.57|0.66|
|22|Publishing, printing and reproduction of recorded media|0.73|0.73|0.82|0.77|
|23|Coke, refined petroleum and nuclear fuel|0.36|0.32|0.36|0.26|
|24|Chemicals and chemical products|0.36|0.28|0.41|0.49|
|25|Rubber and plastic products|0.42|0.57|0.59|0.49|
|26|Other non-metallic mineral products|0.83|0.82|0.84|0.76|
|27|Basic metals|0.09|0.22|0.24|0.19|
|28|Fabricated metal products, except machinery and equipments|0.78|0.81|0.86|0.83|
|29|Machinery and equipment, n.e.c.|0.59|0.63|0.55|0.55|
|30|Office, accounting and computing machinery|0.06|0.09|0.07|0.13|
|31|Electrical machinery and apparatus, n.e.c.|0.22|0.29|0.53|0.53|
|32|Radio, television, and communication equipment and apparatus|0.17|0.20|0.27|0.18|
|33|Medical, precision and optical instruments, watches and clocks|0.16|0.42|0.35|0.31|
|34|Motor vehicles, trailers and semi-trailers|0.09|0.14|0.33|0.21|
|35|Other transport equipment|0.21|0.26|0.28|0.39|
|36|Furniture, manufacturing n.e.c.|0.98|0.98|0.97|0.94|
||Total|0.81|0.79|0.83|0.81|
||Traditional|0.87|0.86|0.88|0.86|
||Modern|0.33|0.40|0.44|0.43|

Notes: See Table 1a.
whether the individual lives in an urban area, and an industry dummy<sup>10</sup> . We then predict wages at the corresponding education levels and five experience levels (at 5, 15, 25, 35, 45 years of experience, corresponding to the average experience in each experience group defined above), in each industry. We perform separate estimations for men and women. We then take the weighted average of the predicted log wage for skilled and unskilled workers. Weights are defined as the average of the labor share of each of the 50 demographic groups in each industry over time.<sup>11</sup> Figure 3 describes the evolution of the wage gap and online exports in some selected countries.<sup>12</sup> The countries were selected so that the variables have similar dimension for illustration purposes. Nevertheless, figure 3 suggests that if the wage gap declined in most countries in our sample, this is not the case in the Philippines, for instance.

The online trade data are borrowed from Lendle et al. (2016), where we only consider fixed price transactions, as the mechanism we have in mind does not go through the auctioning of second-hand goods.<sup>13</sup> It contains bilateral cross-border flows on the eBay platform over the period 2004-2012. Cross border flows represented on average 20 percent of the total value of transactions on the eBay platform during this period. As shown in figure 1 online trade was growing faster than overall trade over the period 2004-2012.

Table 2 provides summary statistics for all the variables used in the estimations of equations (1)-(7). The data source for GDP, average tariffs, and the share of Internet users is the World Bank’s World Development Indicators. Information on distance comes from the CEPII distance database (Mayer and Zignago, 2011).

# **4 Online exports and the wage gap**

Table 3 presents the ordinary least square estimates. All regressions include country and year fixed effects, and standard errors are clustered at the country-level to account for intertemporal correlation in the error term for each country. In all columns online exports has a negative sign and is statistically significant at the 1 percent level. Using the estimates of column (4) which corresponds to equation (1) we find that a 1 percent increase in the share of online exports over GDP decreases

> 10There are ten broad industries in the I2D2: agriculture, mining, manufacturing, public utilities, construction, retail and wholesale, transport, finance, public administration, and others.

> 11See Cruz and Milet (2017) for further details on the construction of the wage gap.

> 12We selected countries based on the comparable magnitudes in terms of changes in the wage gap and online trade. Graphs for other countries show similar patterns.

> 13We also used as a robustness check all online trade flows on eBay which include auction transactions. Results are similar and often larger in magnitude.

8
14 available) is that in the second column of Appendix Table 1, where averages are calculated using import weights.<sup>11</sup> For the period since 1968-69, the Productivity Commission (and its predecessors the Industries Assistance Commission and the Industry Commission) provides estimates of both nominal and effective rates of assistance to manufacturing, for industry subcategories down to the 4-digit level. In addition to tariffs these cover subsidies, bounties and discriminatory sales taxes. Initially they did not cover quantitative restrictions, content plans, etc. (Industries Assistance Commission 1976, p.4), but these are included from 1982-83.

The average nominal rate of assistance on outputs (NRAs) for the whole nonagricultural tradables sector is reproduced in the final column of Appendix Table 1. To get that average, we assumed only (and all) service sectors produce non-tradables, and a zero NRA for non-agricultural primary sectors. For manufacturing, we use the Productivity Commission NRA output estimates from 1968-69 (ignoring their estimates of net assistance via primary factor and intermediate input markets, which in the past decade would have lowered them by about one-fifth), while for earlier years the dutiable import tariff averages estimated by Lloyd are used (the second column in Appendix Table 1). The latter may overstate assistance to the sector in so far as some competing imports come in duty free; but that is assumed to be offset by the numerous non-tariff barriers to imports that were in place in the years prior to 1970. For the two years of overlap in the two series in the late 1960s, they are almost identical (at 23 percent). The

> 11 For an extensive discussion of the strengths and weaknesses of these calculations, including their weighting systems, see the Appendix in Anderson et al. (2007).
_f_<sup>_′_</sup> _> f_ or in more upstream countries _i_ ( _m_ ) with _m > n_ , and where the price _pif_ can be itself re-expressed as the sum of all costs incurred in upstream stages, adjusting for quantities: _qi_ ( _n_ ) _,f pi_ ( _n_ ) _,f_ = �( _m,f_<sup>_′_</sup> ) _>_ ( _n,f_ )<sup>_qi_(</sup><sup>_m_)</sup><sup>_,f′ci_(</sup><sup>_m_)(</sup><sup>_si_(</sup><sup>_m_)</sup><sup>_,f′_).</sup> The connection between the two indexes _N_ and _D_ is clearest if we look at the most downstream stage. For the most downstream country _i_ = 1 and the most downstream firm _f_ = 0 in the country, index _N_ corresponds to a weighted average of _D_ : with the price _pi_ =1 _,f_ =0 =<sup>�</sup> _j_ � _fF_<sup>_′_</sup> _j_ =0<sup>_qif′cj_(</sup><sup>_sjf′_)</sup><sup>_df ′_beingthesumofallupstreamcosts.</sup> As for _D_ , we can also use the model to interpret _Nik_ as a semi-elasticity of w.r.t. to transaction costs, looking at prices instead of quantities. Formally, _Nif_ corresponds to:<sup>34</sup>

**Borders vs. stages:** The _N_ and _D_ calculations are independent of whether or not the shipments in question cross international borders. Since the organization of international production networks is a key question for this literature, and for this study, we propose alternative forms of the indexes in Fally (2012). Using analogous methods to those used in the calculation of _D_ and _N_ we ask how many international borders are crossed for each region-industry pair’s outputs and inputs. D* measures the average number of national borders crossed before final consumption. N* measures the weighted average number of production stages that are embodied in the output of an industry.

Formally, and with most of the variables defined above. _Xik_ represents exports by sector _k_ in _i_ , _Mik_ represents imports, and _Yik_ is sector gross output.

In our model, the equivalent is _Dif_<sup>_∗_reflecting the number of country borders to cross before</sup>

> 34Based on the results in Fally (2012), we should also note that _Nif_ equals the aggregate gross-output-tovalue-added ratio across upstream activities. It can therefore be interpreted as a weighted average of _γ_<sup>_<u>θi</u>_</sup> _i_<sup>across</sup> upstream activities.

38
60

# Table 5

# **Number of People on Various Labour Market Schemes** Poland

|No. of people on<br>schemes ('000)|1990|1991|1992|1993|
|---|---|---|---|---|
|Training/retraining|10.3|68.1|70.2|72.7|
|Intervention works|106.9|36.0|104.5|132.7|
|Public works|||35.5|74.1|
|Sum of people on schemes|117.2|104.1|210.2|279.5|
|Share of scheme<br>participants in total<br>unemployment (%)|20.3|6.4|9.0|10.3|
|Apprenticeship|x|x|400.0|320.0|
|Start-up loans|x|x|9.8|11.7|

- Public Works started in 1992.

x comparable data are not fully reliable.

_Source:_ G6ra (1995).
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26