rafmacalaba/datause-encoder-data huggingface dataset:
# **Boxes**

|Box 1: How Does the Literature Define OJT? ................................................................................................ 5|
|---|
|Box 2: Key Questions Explored in the Literature Review .............................................................................. 6|
|Box 3: The Methodological Challenge of Comparing Returns across Training Programs ........................... 10|
|Box 4: What Type of Workers Receive Training? ........................................................................................ 13|
|Box 5: Evidence of the Presence of Industry-specific General Skills ........................................................... 18|
|Box 6: The Limited Role of Credit Constraints ............................................................................................ 25|

# **Figures**

|Figure 1: Effects of OJT on Productivity and Wage Gains ........................................................................... 13|
|---|
|Figure 2: Effects of Worker Mobility on Productivity and Wages .............................................................. 17|
|Figure 3: Management Practice Scores across Countries ........................................................................... 21|

# **Tables**

|Table 1: Constraints and Market Failures Associated with Providing OJT .................................................... 6|
|---|
|Table 2: Summary of Findings from the Literature on Constraints to Providing OJT ................................... 9|
|Table 3: Summary of Findings from the Literature on OJT ......................................................................... 35|

# **Abbreviations**

|ALMP|Active Labor Market Policies|
|---|---|
|CEO|Chief Executive Officer|
|FDI|Foreign Direct Investment|
|HRM|Human Resource Management|
|IALS|International Adult Literature Survey|
|ICT|Information and communications technology|
|IRR|Internal rate of return|
|MNC|Multinational Corporation|
|MNE|Multinational Enterprise|
|NCC|Non-compete contracts|
|OECD|Organisation for Economic Co-operation and Development|
|OJT|On-the-job training|
|PPP|Public-Private Partnership|
|R&D|Research and Development|
|RPL|Recognition of prior learning|
|SME|Small and medium enterprise|
|TFP|Total Factor Productivity|
|TVET|Technical and Vocational Education and Training|

3
# **Appendix Table 2. Changes in the QFII and RQFII Aggregate Quotas**

Quotas are measured in billions of U.S. dollars. Sources: State Administration of Foreign Exchange, the State Council, 2019 RMB Internationalization Report.

||QFII|||RQF|II|
|---|---|---|---|---|---|
|Date|Total|Increase|Total|Increase|Country|
|Dec 1, 2002|4|4||||
|Jul 11, 2005|10|6||||
|Dec 10, 2007|30|20||||
|Dec 16, 2011|||3|3|HongKongSAR, China|
|Apr 3, 2012|80|50||||
|Apr 3, 2012|||10|7|HongKongSAR, China|
|Nov 13, 2012|||39|29|HongKongSAR, China|
|Jul 12, 2013|150|70||||
|Oct 15, 2013|||51|12|United Kingdom|
|Oct 22, 2013|||58|7|Singapore|
|Mar 26, 2014|||69|12|France|
|Jul 3, 2014|||81|12|South Korea|
|Jul 7, 2014|||93|12|Germany|
|Nov 3, 2014|||97|4|Qatar|
|Nov 8, 2014|||104|7|Canada|
|Nov 17, 2014|||111|7|Australia|
|Jan 21, 2015|||119|7|Switzerland|
|Apr 29, 2015|||126|7|Luxembourg|
|May25, 2015|||133|7|Chile|
|Jun 27, 2015|||140|7|Hungary|
|Oct 31, 2015|||146|6|South Korea|
|Nov 17, 2015|||153|7|Singapore|
|Nov 23, 2015|||161|7|Malaysia|
|Dec 14, 2015|||168|7|United Arab Emirates|
|Dec 17, 2015|||175|7|Thailand|
|Jun 7, 2016|||211|36|United States|
|Dec 21, 2016|||219|7|Ireland|
|Jul 4, 2017|||252|33|HongKongSAR, China|
|May9, 2018|||281|29|Japan|
|Jun 5, 2019|||288|7|The Netherlands|
|Jan 14, 2019|300|150||||
|Sep 10, 2019||Unlimited, im|plemented on Ma|y 7, 2020||
Bazzi, Samuel, Lisa Cameron, Simone Schaner and Firman Witoelar (2021) “Information, intermediaries and international migration”, Mimeo. UCSD.

Beam, Emily, David McKenzie and Dean Yang (2016) “Unilateral Facilitation Does Not Raise International Labor Migration from the Philippines”, _Economic Development and Cultural Change_ , 64(2): 323-68.

Becker, Sasha and Andreas Ferrara (2019) “Consequences of forced migration: a survey of recent findings”, _Labour Economics_ 59: 1-16.

Beegle, Kathleen, Joachim De Weerdt and Stefan Dercon (2011). “Migration and Economic Mobility in Tanzania: Evidence from a Tracking Survey.” _Review of Economics and Statistics_ , 93, 1010–1033 Bewley, Truman (2002) “Knightian Decision Theory: Part 1”, _Decisions in Economics and Finance_ 25: 79–110 Borjas, George (1987) “Self-selection and the earnings of immigrants”, _American Economic Review_ 77(4): 531-553.

Bryan Gharad, Shyamal Chowdhury and A. Mushfiq Mobarak (2014). “Underinvestment in a Profitable Technology: The Case of Seasonal Migration in Bangladesh.” _Econometrica_ , 82(5): 1671–1748 Bryan Gharad and Melanie Morten (2019) “The Aggregate Productivity Effects of Internal Migration: Evidence from Indonesia”, _Journal of Political Economy_ , 127(5), 2229-2268.

Bundervoet, Tom (2018) “Internal Migration in Ethiopia : Evidence from a Quantitative and Qualitative Research Study” World Bank, Washington, DC. <u>https://openknowledge.worldbank.org/handle/10986/32097</u> Cai, Shu (2020) “Migration under liquidity constraints: Evidence from randomized credit access in China”, _Journal of Development Economics_ 142: article 102247.

Chapela, Jorge González (2022) “Is there a patience premium on migration?”, _Empirical Economics_ <u><mark>https://doi.org/10.1007/s00181-021-02196-z</mark></u> Chyn, Eric (2018): “Moved to Opportunity: The Long-Run Effect of Public Housing Demolition on Labor Market Outcomes of Children,” _American Economic Review_ , 108, 3028–3056 Clemens Michael (2011) “Economics and emigration: trillion-dollar bills on the sidewalk?”, _Journal of Economic Perspectives_ 25(3): 83–106 Clemens, Michael, Claudio Montenegro, and Lant Pritchett (2019). “Bounding the Price Equivalent of Migration Barriers”, _Review of Economics and Statistics_ , 101 (2): 201–213 22
**Table 15 Cross-tabulation of decile rankings of pension capital accumulation according to different assumptions about contribution rules**

||=I|1st|_______<br>2nd|3rd|JO<br>4th|INT C<br>5th|ONTRI<br>6th|BUTIO<br>7th|NS<br>8th|h||
|---|---|---|---|---|---|---|---|---|---|---|---|
|||||||||||9t|Top|
|w<br>N|lst<br>**2nd**|49.2<br>9.1|15.4<br>225|11.0<br>144|7.5<br>103|6.3<br>69|2.8<br>|3.8<br>|2.5<br>|0.6<br>|0.9<br>|
|C|3d|<br>|.<br>|.<br>|.<br>|.<br>|6.3|3.1|3.1|3.1|**1.3**|
|<br>**0**<br>|r<br>|14.1|24.1|13.2|12.2|**11.9**|9.7|15.3<br>-|3.8|4.1|1.6<br>|
|N<br>T|4th<br>5th|4.1<br>2.5|18.1<br>11.6|22.8<br>138|13.1<br>238|9.4<br>160|9.7<br>141|6.9<br>|8.2<br>|5.9<br>|11<br>1.9<br>|
|I|6th|<br>06|<br>69<br>|.<br>156|.<br>|.<br>|.<br>|7.2<br>|6.6<br>|3.5|0.9|
|<br>U|<br>7h|.<br>|.<br> <br>|.<br> <br>|14.1<br>|18.1<br>|19.1|8.4|8.4|5.9|2.8|
||t|0.3|1.3|7.5<br>|12.2|13.4|16.3|21.9|12.2|9.1|5.9|
|T<br>O|8th<br>9th|0.0<br>00|0.3<br>|1.6<br>|6.6<br> <br>|15.4<br>|13.5<br>|17.9<br>|25.4|14.7|4.7|
|<br>N<br>S<br>|<br>T|.<br>--<br>|0.0<br>|0.0<br>|0.3<br>|2.5<br>|8.8<br>|22.8<br>|20.0<br>|30.3|15.3|
|<br>|op|0.0|0.0|0.0|0.0|0.0|0.0|2.8|9.7|22.8<br>|64.6|

Table 15 shows that there is considerable movement across the total income distribution in the relative positions of individuals according to the rules of pension fund accumulation. This is not surprising given the changes shown in Table 14 in the proportions of men and women affected by the joint contribution rule. Further disaggregation by gender is instructive. When ranked on individual contributions, very few men find themselves in the lowest quintile of the distribution (1.7%) and only 21.8% are located in the bottom half. However, if pension contributions during marriage are split evenly between the couple regardless of the source of the income, men's position within the total distribution changes markedly. In this case 13.3% of men are dragged into the bottom quintile and 44.3% into the bottom half. If joint contributions acted to entirely eliminate gender inequality in a funded pension system, we would expect on average 10% of men and 10% of women in each decile grouping. In practice, as Table 16 shows, even after contribution sharing men are still over-represented in the upper deciles, though much less so than on the basis of 55
1,689 firms. By country, our sample size ranges from a low of 71 firms in the Philippines to a high of 309 firms in Malaysia (see Table 1).

We calculate external capital ratios for equity and both short-term and long-term debt. External equity is defined as the market value of equity multiplied by one minus the average percentage of market capitalization closely held as reported by Dahlquist et al. (2003, p. 95). Following LLSV, we calculate capital ratios by dividing external capital measures by total sales.<sup>5</sup> We calculate several control variables that have consistently been shown to be related to capital structure such as firm size (log of sales in U.S. dollars), asset tangibility (fixed assets as a percent of total assets), profitability (operating margin), and the ratio of market-to-book value of equity.

WorldScope does not have data on the currency denomination of debt so we rely on a database created by Swiss Bank Corporation – Warburg Dillon Reed (SBC-WDR) that estimates the level of both short-term and long-term debt in local and foreign currency. This is the same database examined by Allayannis, Brown, and Klapper (2003) and includes data on 315 firms from the same East Asian countries; 179 firms out of the 315 have foreign currency debt. Additional details regarding these data are available in the appendix of Allayannis, Brown, and Klapper (2003).

Our measure of legal effectiveness is the “Legality” index generated by Berkowitz, Pistor, and Richard (2003). The variable is generated by taking the first principal component of the five “enforcement” variables reported by LLSV (1998). Specifically, _Legality = 0.381*(Efficiency of the Judiciary) + 0.579*(Rule of Law) + 0.503* (Corruption) + 0.349*(Risk of Expropriation) + 0.384*(Risk of Contract Repudiation)_ This measure (the first principal component) accounts for 84.6% of the variance in the five measures. Berkowitz, Pistor, and Richard (2003) do not report a legality index for China, therefore we compute this value by regressing the _International Country Risk_ (ICR) Financial

> 5 LLSV also examine the external capital measures standardized by profits but we do not, because we concentrate on firm-level analysis and not all firms are profitable.

9
# Table 2

# Reductions in LDC ExRosure<sup>at 29 Banks Selected Among the Largest</sup>

||Total L<br>in M<br>(unless ot<br>|DC Exposur<br>illions<br>herwise not<br> <br>|e<br>ed)|Exposure<br>June<br> <br>Septe|Redu<br>1987<br>mber|ctions<br>to<br>1988|
|---|---|---|---|---|---|---|
||June 87|<br>Sept 88|||||
|Citicorp<br>|$14,600<br>|<br>$12,100<br> <br>|<br>|$2,500<br>1354||17%<br>13%|
|BankAmerica|10,354<br>|<br>9,000<br> <br>|<br>|,<br>|||
|Manufacturers Hanover|9,234<br>|<br>8,688<br> <br>|<br>|546<br>||6%<br>|
|Chase Manhattan<br>|8,740<br>|<br>7,950<br> <br>|<br>|790<br>45||9%<br>1%|
|Chemical Banking|5,945<br>|<br>5,900<br> <br>|<br>|<br>|||
|J.P. Morgan|5,400<br>|<br>4,700<br> <br>|<br>|700<br>||13%<br>|
|Bankers Trust<br>|4,000<br>|<br>4,000<br> <br>|,<br>|0<br>691||0%<br>22%|
|First Chicago<br>|3,120<br>|<br>2,429<br> <br>|<br>|<br>||17%|
|Continental Illinois|2,400|<br>2,000<br>|<br>|400<br>|||
|Irving Bank Corp<br>|1,950<br>|<br>1,890<br> <br>|<br>|60<br>||3%<br>13%|
|Mellon|1,600<br>|<br>1,386<br> <br>|<br>|214<br>|||
|Security Pacific|2,200<br>|<br>1,260<br> <br>|<br>|940<br>||43%<br>|
|Bank of Boston|1,400<br>|<br>1,000<br> <br>|<br>|400<br>||29%<br>|
|First Interstate|1,606<br>|<br>996<br> <br>|<br>|610<br>||38%<br>|
|Wells Fargo|1,909|<br>760<br>|<br>|1,149<br>||60%<br>|
|Bank of New York (a)<br>|544<br>|<br>470<br> <br>|<br>|74<br>||14%<br>|
|Republic of New York|487<br>|E<br>461<br> <br>|E<br>|26<br>||5%<br>|
|PNC Financial (c)|481|E<br>291<br>|<br>|190<br>||40%<br>|
|First Fidelity (b)<br>|237<br>|<br>177<br> <br>|<br>|60<br>||25%<br>|
|Northern Trust (a)<br>|321<br>|<br>149<br> <br>|<br>|172<br>||54%<br>|
|Southeast Banking (b)|215<br>|<br>117<br> <br>|<br>|98<br>||46%<br>|
|MNC Financial|223<br>|<br>107<br> <br>|<br>|116<br>||52%<br>|
|Bank of New England|307<br>|<br>106<br> <br>|<br>|201<br>||65%<br>|
|Valley National|155|<br>62<br>|<br>|93<br>||60%<br>|
|Fleet/Norstar|145|<br>52||93||64%<br>|
|First Wisconsin|289<br>|<br>17<br> <br>|<br>|272<br>||94%<br>|
|Norwest Corp. (b)|515|<br>16||499||97%<br>|
|First Wachovia|212|<br>8|(d)|<br>204<br>||96%<br>|
|NCNB Corp.|247|<br>4||243||98%|
||$78,837|<br>$66,095||$12,742||16%|

(a) Includes loans only (b) Includes non-trade-related credits only (c) Includes medium- and long-term<sup>LDC loans only</sup> (d) The exposure totals at<sup>these dates were fully reserved against</sup> NOTE: Exposures only in dollars<sup>(and not in local currency) are included</sup> E - Estimate 20
# **D.1 Supplementary Figures**

<!-- Start of picture text --> a)<br><!-- End of picture text --> <!-- Start of picture text --> 140 Fertilizers (Deterministic-Baseline) 100 Difference between Stochastic and Deterministic-Baseline Solutions<br>Range of all sample paths<br>average<br>10% quantile<br>6 50% quantile<br>90% quantile<br>Stochastic-optimistic<br>Stochastic-pessimistic<br>4<br>120 80<br>2<br>0<br>100 60<br>-2<br>Fertilizers (million tons)<br>Ratio of Fertilizers to -4<br>Food Crop Land Area (kg per Ha)<br>80 40<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Year Year<br>million tons kg per Ha<br>Ratio of Fertilizers to Food Crop Land Area (kg per Ha)<br><!-- End of picture text --> b) <!-- Start of picture text --> Biofuels (Deterministic) Difference between Stochastic and Deterministic-Baseline Solutions<br>Deterministic-baseline 20 Range of all sample paths<br>35 Deterministic-optimisticDeterministic-pessimistic average10% quantile<br>15 50% quantile<br>90% quantile<br>30 Stochastic-optimistic<br>10 Stochastic-pessimistic<br>25<br>5<br>20<br>0<br>15<br>-5<br>10<br>-10<br>5<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Year Year<br>c)<br>2G Biofuels (Deterministic) Difference between Stochastic and Deterministic-Baseline Solutions<br>1600<br>Deterministic-baseline Range of all sample paths<br>Deterministic-optimistic 200 average<br>1400 Deterministic-pessimistic 10% quantile<br>150 50% quantile<br>90% quantile<br>1200 100 Stochastic-optimistic<br>Stochastic-pessimistic<br>50<br>1000<br>0<br>800<br>-50<br>600 -100<br>-150<br>400<br>-200<br>200 -250<br>-300<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Year Year<br>26<br>million tonnes of oil equivalent million tonnes of oil equivalent<br>million tonnes of oil equivalent million tonnes of oil equivalent<br><!-- End of picture text --> Figure D.1: Consumption of Fertilizers and Biofuels
Neither methodology proposed by Nagengast and Sterher (2016) manages to break up the whole domestic and foreign value-added exported by a country across the different bilateral flows. However, this probably goes beyond the scope of their analysis, which focuses on the value-added components produced and absorbed by two bilateral partners. Since an important aspect in their study is the role of final demand in generating bilateral trade balances, a sink-based approach seems ideally suited. As mentioned, the proper measure of this component is given by the **VAXIM** of equation (39), which differs from the measure proposed by Nagengast and Sterher (2016). In fact, they only classify as ‘domestic value-added absorbed by direct importers’ what is embedded in goods that do not leave this country again, assigning the remainder to the double counted component. In this way, they do not take into account what we have classified in the **2b** and **3c** components in equation (D.2), underestimating the domestic value-added absorbed by direct importers.

# **6 Concluding remarks**

The diffusion of international production networks over the last three decades calls for new tools in order to evaluate supply and demand relationships between countries which can no longer be adequately gauged by gross trade flows. For this reason, inter-country input-output tables and new methodologies that exploit these data have been developed to measure trade in value-added terms and countries’ participation in GVCs. These tools have been extensively used in a large number of applications. However, on the one hand, several empirical issues have been not properly addressed so far; on the other hand, the emergence of different methodologies for the value-added accounting of trade flows has raised doubts as to the correct way of measuring the phenomena.

In this paper we propose a general scheme for breaking down bilateral and sectoral exports flows according to the source and the destination of their valueadded content. This framework can be differentiated according to alternative approaches and perspectives that are instrumental to addressing different empirical issues. These different approaches are reflected in distinct ways to distinguish between ‘value-added’ and ‘double-counted’ terms in gross trade flows. Operationally, this differentiation is obtained by changing the definition of the sectoral-geographical perimeters according to which the items that cross these boundaries more than once are classified as ‘double counted’. We consider more in detail three alternative cases: in the first one, the boundaries are defined at the level of the exporting 42
6. When using the _pdslasso_ command, be aware of the lack of a degrees of freedom adjustment. Otherwise one can end up with smaller standard errors after using this command compared with using OLS even when no additional controls are selected. More generally, the implementation details can differ across software packages, and so it is useful to pre-specify exactly how the method will be implemented.

After following these steps, we see PDS Lasso as a useful robustness check for many field experiments, providing a less ad hoc way of selecting additional control variables on top of any lagged variable and randomization strata. Most of the time it should make very little difference to the estimated coefficients and standard errors. When it does make a sizeable change in the coefficients, this can provide a useful warning for researchers that they can no longer rely simply on random assignment to justify their results.

# **References**

- **Ahrens, Achim, Christian Hansen, and Mark Schaffer** , “Pdslasso and ivlasso: Progams for post-selection and post-regularization OLS or IV estimation and inference,” http://ideas.repec.org/c/boc/bocode/s458459.html 2018. updated 2020.

- **Bai, Yuehao** , “Optimality of matched-pair designs in Randomized Controlled Trials,” _American Economic Review_ , 2022, _112_ (12), 3911–3940.

- **Belloni, Alexandre and Victor Chernozhukov** , “High Dimensional Sparse Econometric Models: An Introduction,” 2011. arXiv:1106.5242v2.

- **, Daniel Chen, Victor Chernozhukov, and Christian Hansen** , “Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain,” _Econometrica_ , 2012, _80_ , 2369–2429.

- **, Victor Chernozhukov, and Christian Hansen** , “High-Dimensional Methods and Inference on Structural and Treatment Effects,” _Journal of Economic Perspectives_ , 2014, _28_ (2), 29–50.

- **, , and** , “Inference on treatment effects after selection among high-dimensional controls,” _Review of Economic Studies_ , 2014, _81_ , 608–650.

- **, , , and Damian Kozbur** , “Inference in High-Dimensional Panel Models With an Application to Gun Control,” _Journal of Business and Economic Statistics_ , 2016, _34_ (4), 590–605.

- **Bloniarz, Adam, Hanzhong Liu, Cun-Hui Zhang, Jasjeet Sekhon, and Bin Yu** , “Lasso adjustments of treatment effect estimates in randomized experiments,” _PNAS_ , 2016, _113_ (27), 7383–7390.

25
# _Long-term revenue projections_

Table 2 provides projections for domestic revenues as a share of GDP in 2010 and 2015.<sup>12</sup> The main assumptions underlying these projections are as follows. First, with increased marginal tax rates, the tax collected from individual income tax as a share of labor income rises from the current level of slightly over 4% to 8% by 2011, and by a further 0.25% of labor income per year thereafter, to reflect what should be the natural elasticity of this tax handle. Formal sector labor income as a share of GDP is assumed to be constant. Secondly, VAT is boosted slightly by the net impact of higher electricity tariffs. Thirdly, other tax handles grow broadly in line with nominal GDP, before the impact of tax administration reforms. Fourthly, the gains from tax administration reforms are projected at a cumulative 1% of total tax revenue per year starting in 2008, hence by 2006 they boost tax revenues by 9%. Fifthly, the revenue projections also include the projected revenue earned from government’s capital investment in the two major hydropower projects, and the surplus in the form of taxes or royalties which can be earned from electricity exports. The net impact is that domestic revenues increase by 3.9% of GDP, from 18.8% of GDP in 2006 to 22.7% in 2016.

# _Grants_

Donor grants to the government budget have been low, although project grants may be under-reported. Excluding the MDRI debt relief received in 2006, grants to the budget amounted to 1.8% of GDP on 2006. The majority of donor grants are disbursed to agencies outside the government budget, such as community based organizations, in part because of concerns about fiduciary weaknesses in the budget.<sup>13</sup> Future levels of grant aid to the budget, the form of budget support and project aid, could be boosted if donor governments increase their overall aid to Tajikistan, in line with commitments made at international fora to expand aid in real terms to the poorest countries, and/or if a larger share of donor aid were to be channeled to the government budget.

> 12 A detailed explanation of the assumptions underlying these projections can be found in World Bank (2006: chapter 3).

> 13 An estimated $114 million of grant aid from donor organizations was disbursed outside the budget in 2005 compared to $45 million of grants (excluding MDRI debt relief) disbursed to the budget in the form of both budget support and project aid. The estimate for the non budget grants is a minimum because data were not available from all donors.

20