rafmacalaba/datause-encoder-data huggingface dataset:
of industrial country insurers.<sup>34</sup> Further measures could involve hospitals and medical boards collecting data on outcomes and costs so that physician performance could be tracked using tiered network programs such as the ones used by insurers in the US (see, e.g., Fronstin, 2003, and Robinson, 2003).<sup>35</sup> Familiarity with doctors from certain regions could also help in removing concerns about the quality of care. For example, many British nationals reportedly seek treatments in India because they are familiar with the expertise of doctors of Indian origin in the UK.<sup>36</sup> It may be possible for US insurers to offer a health plan option, for example, where an immigrant consumer is offered the option of undergoing certain treatments in his/her home country, where she/he is familiar and comfortable with the health care system, physicians, culture.

# **8.  Conclusion**

Our study has shown how existing health insurance plans inhibit the consumption of health care abroad by consumers. Modifications of these health insurance contracts to adequately cover the costs of care received abroad would give consumers adequate incentives to undergo tradable treatments overseas. We should emphasize that we are not suggesting that insurers should force patients to seek treatments overseas in order to cut costs but that insurers should offer patients the _option_ of obtaining care overseas.

Using price data from the Medicare program and Vanbreda International, we undertook a worldwide price comparison of 15 low-risk, highly tradable surgeries. We included only non-emergency and routine surgeries, such as knee and shoulder arthroplasties, skin lesion excisions, toe-deformity repairs, and cataract extractions. The magnitude of the gains from trade in just these 15 surgeries was estimated to be over $1.4 billion, even if only one in ten US patients chose to undergo treatment abroad rather than in the US. Of these annual savings, $690 million would accrue to the Medicare program alone.

The scope for trade would be greatly enhanced if quality abroad were further improved, and credibly signaled through international accreditation of export-oriented hospitals and 34 Currently, stages 1 and 2 of the USMLE can be taken abroad, while plans to set up overseas locations for the NCLEX-RN are still in the pipeline (Sources: National Board of Medical Examiners and National Council of State Boards of Nursing).

35 For example, Aetna’s Aexcel program rates physicians by regional network (e.g., the Baltimore/DC/Northern VA region) and medical specialty, after controlling for patient volume, clinical performance, cost-efficiency, and network adequacy (see Aetna, 2004). This is evidence of that fact that insurers are interested in identifying high-quality cost-efficient domestic providers in the US. If foreign hospitals and medical boards were to collect and report outcomes and cost data on treatments provided by their physicians, then insurers could rate foreign providers on a similar basis and thereby maintain quality and cost standards for the overseas network as well.

36 The Guardian, May 11, 2004.

25
the number of FDI projects. The positive FDI-specific effects of financial development are the strongest at the intensive margin of FDI. On the other hand, the positive general effects of financial development are the strongest at the extensive margin of FDI.

These observations explain why we have found in column (3) that the contribution of the direct effects to the overall positive effects of SFD and DFD on the relative volume of bilateral FDI in financially vulnerable sectors is much larger than that of the indirect effects. They also underline that a fall in output created by a negative financial shock is of little importance relative to tighter credit conditions. Finally they indicate that the relative response of FDI to changes in financial development is likely to be much larger at the intensive margin of FDI than at the extensive margin of FDI. In the context of international trade, Manova (2013) reaches similar conclusions.

# **5.2.5 Financial development, FDI, and international trade**

We end this section by investigating whether SFD and DFD influence exports and FDI, two alternative modes of internationalization, in the same way. We use as dependent variable the sector-specific value of bilateral trade in columns (10)-(12) of Table 4. Data come from the CEPII _Trade and Production Database_ . In columns (10) and (11), we use trade data for the 23 ISIC sectors listed in Table 5 (Appendix A). In column (12), we aggregate the data into 13 broad sectors, in the same way as we have done for greenfield FDI. To ensure that the trade results can be compared with our FDI results, we restrict the sample to those country-pair-year observations for which we also observed FDI.

On the source side, our results mirror those of Manova (2013). Higher SFD increases the relative volume of bilateral exports in financially vulnerable sectors and the trade-specific effect of SFD corresponds to about 70% of the overall effect of SFD. A comparison of the estimates reported in column (3) and in columns (11) and (12) indicates that greenfield FDI is relatively more sensitive to a rise in SFD than exports. This stronger effect of SFD on FDI than on exporting is in line with the predictions and empirical evidence of Buch et al.

38
**Table 11. Changes in employment rates by levels of exposure to ICT**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||A|ll|All<br>M|Ages<br>en|Wo|men|
|Routine content|20.12*|23.17**|17.71*|18.47**|21.83|26.15*|
||(11.54)|(10.32)|(9.887)|(8.321)|(14.33)|(13.53)|
|Internet|0.0878|0.118|0.138|0.125|0.0577|0.118|
||(0.0850)|(0.110)|(0.0934)|(0.114)|(0.0957)|(0.118)|
|Routine x Internet|‐2.064**|‐2.582**|‐1.607|‐1.970*|‐2.559*|‐3.078**|
||(1.050)|(1.265)|(1.002)|(1.118)|(1.314)|(1.540)|
||A|ll|15‐24<br>M|years<br>en|Wo|men|
|Routine content|17.80*|21.10**|16.59*|16.53**|18.54|22.50*|
||(10.64)|(9.963)|(8.867)|(7.561)|(13.77)|(13.07)|
|Internet|0.222**|0.235*|0.277**|0.278**|0.180|0.216|
||(0.108)|(0.136)|(0.111)|(0.135)|(0.126)|(0.152)|
|Routine x Internet|‐2.930***|‐3.003**|‐2.212**|‐2.082*|‐3.528**|‐3.795**|
||(1.073)|(1.206)|(1.051)|(1.142)|(1.389)|(1.543)|
||A|ll|25‐54<br>M|years<br>en|Wo|men|
|Routine content|20.79*|22.65**|17.93**|16.17**|22.26|26.35**|
||(10.90)|(9.652)|(8.643)|(8.005)|(13.97)|(12.83)|
|Internet|0.0688|0.123|0.124|0.124|0.0342|0.112|
||(0.0828)|(0.114)|(0.0997)|(0.136)|(0.0916)|(0.114)|
|Routine x Internet|‐2.251**|‐3.046**|‐1.998*|‐2.632*|‐2.529*|‐3.226**|
||(1.083)|(1.365)|(1.125)|(1.388)|(1.302)|(1.553)|
||A|ll|M<br>55‐64|en<br> years|Wo|men|
|Routine content|27.28|29.61|22.57|21.22|29.81|33.63|
||(17.25)|(19.09)|(15.20)|(16.17)|(21.34)|(22.06)|
|Internet|0.0338|0.0747|0.125|0.0920|0.0157|0.0810|
||(0.133)|(0.165)|(0.152)|(0.182)|(0.145)|(0.165)|
|Routine x Internet|‐1.352|‐2.253|‐1.094|‐1.728|‐1.918|‐2.561|
||(1.698)|(2.143)|(1.687)|(2.084)|(2.054)|(2.358)|
|Country characteristics|NO|YES|NO|YES|NO|YES|
|Observations|300|296|300|296|300|296|

Note: OLS coefficients from a cross-country regression using the employment rate as the dependent variable.

# **7. Conclusions**

This paper contributes to a growing body of literature investigating the skill content of jobs. While most articles impute US-based measures of the task content of occupations to other countries, we use harmonized data on the task content of jobs for 11 developing countries. We find that indexes based on the US and on developing countries lead to similar conclusions regarding the stock, changes and drivers of the non-routine cognitive and routine manual content of jobs. However, the 30
schooling abroad during the Gulf War. Pandemic school closures led to remote schooling. Third, both episodes harmed mental health, though the nature and extent of the harm remain unknown (Bilo et al. 2021: 9–10; de Miranda et al. 2020; Vindegaard & Benros 2020). Last, school disruptions did not lead to universal grade repetition. After the catch-up period in the 1991/92 academic year, students continued their schooling as if the war had not happened. Similarly, during this pandemic, students in Kuwait are continuing without repeating grades.

We concede that the two episodes are not identical. For example, it is impossible to give a precise comparison of pandemic remote schooling with its heterogeneous quality across students and the schooling abroad that some students received during the war. Instead, we show that the episodes are meaningfully similar in terms of the order of magnitude of the schooling shocks they represent. The meaningful similarities between the two episodes, we argue, justify our use of the Gulf War experience to simulate the effect of pandemic-related school disruptions on labor-market outcomes.

**Table 1: Comparing the two schooling disruptions in Kuwait**

||Gulf War schoolingshock|COVID-19 schoolingshock|
|---|---|---|
|School closures|1 school year (1990/91)|~4 months (spring 2020) +<br>1 school year (2020/21)|
|Schooling during closures|A large, though unknown,<br>number of students studied<br>abroad|Online/remote schooling|
|Other factors|War-related mental health<br>problems|Pandemic-related mental<br>health problems|
|Post-shock remediation|Tutoring and catchup<br>schoolingin 1991/92|Pandemic is still ongoing, but<br>grade repetitions are unlikely|

Sources: Alhouti 2020; de Miranda et al. 2020; Kuwait News Agency 2020; Vindegaard & Benros 2020; Bilo et al. (2021); Kuwait Times 2021.

# **3. Estimating the Costs of Schooling Disruptions**

6
# **Table 1:** Utilization, Satisfaction, and Health Outcomes

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>Difference|(6)<br>Joint<br>_F_-test (_p_)|(7)<br>N|
|---|---|---|---|---|---|---|---|
|General utilization|0.000|0.112|0.126|0.099|0.026|7.054|4496|
||(1.000)|(0.031)<sup>_∗∗∗_</sup><br>[0.005]<sup>_∗∗∗_</sup>|(0.034)<sup>_∗∗∗_</sup><br>[0.003]<sup>_∗∗∗_</sup>|(0.037)<sup>_∗∗∗_</sup><br>[0.032]<sup>_∗∗_</sup>|(0.033)|(0.001)<sup>_∗∗∗_</sup>||
|Maternal utilization|0.000|0.061|0.175|_−_0.043|0.218|4.128|888|
||(1.000)|(0.064)|(0.077)<sup>_∗∗_</sup>|(0.076)|(0.081)<sup>_∗∗∗_</sup>|(0.017)<sup>_∗∗_</sup>||
|||[0.327]|[0.068]<sup>_∗_</sup>|[0.548]||||
|Health outcomes|0.000|0.053|0.146|_−_0.037|0.184|6.318|5053|
||(1.000)|(0.051)|(0.056)<sup>_∗∗∗_</sup>|(0.059)|(0.056)<sup>_∗∗∗_</sup>|(0.002)<sup>_∗∗∗_</sup>||
|||[0.327]|[0.045]<sup>_∗∗_</sup>|[0.548]||||
|Satisfaction|0.000|0.101|0.091|0.109|_−_0.018|2.876|5052|
||(1.000)|(0.042)<sup>_∗∗_</sup><br>[0.038]<sup>_∗∗_</sup>|(0.049)<sup>_∗_</sup><br>[0.095]<sup>_∗_</sup>|(0.049)<sup>_∗∗_</sup><br>[0.048]<sup>_∗∗_</sup>|(0.048)|(0.058)<sup>_∗_</sup>||

_Notes:_ Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Multiple-inference corrected q-values that adjust for the false discovery rate within treatment arm are shown in square brackets. Column (5) presents an _F_ -test for the joint significance of CM and NFA, with the associated _p_ -value in parentheses. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.
Table 7: Heterogeneity by fishing productivity (for non-owners)

|VARIABLES||Fish|in 07||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|AGGSHOCK|2.066**|1.112|-0.626**|-0.545**|
||(0.938)|(0.843)|(0.254)|(0.213)|
|AGGSHOCK*Ln (boat length pre-tsunami)|**-1.360*****||||
||**(0.446)**||||
|Ln (boat length pre-tsunami)|0.726***||||
||(0.263)||||
|AGGSHOCK*Ln (engine capacity pre-tsunami)||**-0.668****|||
|||**(0.295)**|||
|Ln (engine capacity pre-tsunami)||0.180*|||
|||(0.107)|||
|AGGSHOCK*Total fish catch/length pre-tsunami|||**-0.00473**||
||||**(0.00503)**||
|Total fish catch/length pre-tsunami|||0.00295*<br>(0.00173)||
|AGGSHOCK*In-shore fishing skill||||**-0.725*****|
|||||**(0.254)**|
|In-shore fishing skill||||-0.00111|
|||||(0.111)|
|D_bad house in 07|-0.195**|-0.168*|-0.236**|-0.229**|
||(0.0952)|(0.0883)|(0.0989)|(0.0897)|
|Boat owner and captain pre-tsunami|0.116|0.0367|0.0947|-0.0189|
||(0.107)|(0.104)|(0.114)|(0.112)|
|Ln(number of NGOs in 2007)|-1.048***|-0.989***|-1.181***|-0.957***|
||(0.270)|(0.229)|(0.303)|(0.258)|
|D_debt to Toke Bangku|0.177|0.183|0.305**|0.170|
||(0.195)|(0.151)|(0.149)|(0.151)|
|High education pre-tsunami|-0.0511|-0.125|-0.240***|-0.103|
||(0.126)|(0.108)|(0.0789)|(0.0940)|
|Number of surviving captains in 05|0.00960***|0.0113***|0.0155***|0.0115***|
||(0.00322)|(0.00310)|(0.00435)|(0.00400)|
|Observations|127|135|135|147|
|Sample||Ori|ginal||
|Mean of dependent variable|0.441|0.437|0.444|0.449|
|Seasonality is controlled|Y|Y|Y|Y|
|Regional fixed effects|Kec|Kec|Kec|Kec|

*** p<0.01, ** p<0.05, * p<0.1; All estimates are marginal effects of probit model. Robust standard errors clustered at the village level are in parentheses.

AGGSHOCK=Fraction of bad aid boats
- 16-

performance: (i) the real **GDP** growth rate; **(ii)** the ratio of domestic investment to GDP; (iii) the ratio of domestic savings to GDP; and (iv) the ratio of exports to GDP.

## **_V.2 Policy Indicators_**

# **V.2.1 Real Effective Exchange Rate**

Table **2** below gives the comparative results on movements of real effective **exchange rates** for the **five** countries, including Tanzania.

**Table 2 Percentage Changes** In **Real Effective Exchange Rates (annual average)**

||1981-85|1986-90|
|---|---|---|
|Ghana|**_5.9_**|**-15.2**|
|Kenya|0.4|-7.1|
|Malawi|**-0.4**|**-1.0**|
|Uganda|-23.2|**-6.0**|
|Tanzania|16.1|-27.8|

Source: IMF and World Bank data.

The comparison shows that real exchange rate adjustment has played a role in all five countries under review. Tanzania, in part because of the size of the initial distortions, has made the largest adjustment, with a real depreciation of the shilling averaging 27.8 percent per annum during 1986-90, in stark contrast to its appreciation by 16.1 percent per annum during 1981-85.

# **V.2.2 Current Account Deficit**

Figure 4 presents data on the current account deficit (after grants) as a percentage of GDP for the **fi -** countries under comparison. It shows that the initial current account deficit in Tanzania was much larger than in the other countries, except Malawi. In the reform period, Tanzania's current account deficit has widened. Except for Malawi, this also occurred in the other reforming countries, and may not necessarily indicate a 'worsening" of the external situation if all that it reflects is that
**<u>Figure 1.</u>** <u>A general framework to consider greening a global value chain</u>

<!-- Start of picture text --> lobbies<br>Governments<br>Environmental<br>energy policy policy<br>Trade/industrial<br>policy<br>competititors Financial and NGOs /<br>communities<br>business services<br>Local/global infrastructures : institutions, cultures, education, health, transportation, communication, energy, water<br>raw customers<br>components assembly retailers<br>materials / end users<br>recovery /<br>air & water pollution<br>site cleanup abatement goods and reprocessing disposal<br>services downstrea<br>m supply<br><!-- End of picture text --> In a landmark document published in 1999, the OECD and Eurostat jointly define the EGS industry as follows (OECD/Eurostat 1999, p. 9): “(...) the set of activities which produce goods and services to measure, prevent, limit, minimize or correct environmental damage to water, air, and soil, as well as problems related to waste, noise and eco-systems. These include cleaner technologies, products and services which reduce environmental risk and minimize pollution and resource use.” Tables 1 and 2 below respectively provide data on revenues per segment and global trade flows for this industry. Many more figures and facts can be found in “EBI Report 2020” of Environmental Business International (2011), and in Durand and Sinclair-Desgagné (2012).

The EGS industry’s global revenues totaled USD 858 billion in 2012. The United States and the European Union each account for 38% of this amount, Japan for 17%, China for 3,2% and India for 2,5%. Waste management, followed by potable water and wastewater management, were the most important activities, both in terms of income and employment. Despite the recent economic downturn, employment in EGS-related activities seems to have done rather well, with an increase of 3% from 2007 to 2008. The EGS industry now employs more than 1.7 million people and accounts for 2.7% of GDP in the United States and in Europe. International trade in environmental goods has also grown faster, on average, than trade in all merchandised products. Historically, developed countries have been net exporters of environmental goods and services, and developing countries net importers (Bahar et al., 2013). However, with the growth of exporters like Brazil, China and India, the trade is becoming more balanced.

4
# **Rising Growth, Declining Investment:** **_The Puzzle_ of the Philippines**

**Breaking the “** **_Low-Capital-Stock_ ”** **_Equilibrium_**

Alessandro Magnoli Bocchi<sup>1</sup> _The World Bank_

**JEL Classification:** F-43, E-22, D-72, H-54, O-11, C-70

**Key words:** Economic Growth, Investment, Capital Stock, Remittances, Rent-capture

> 1 The findings, interpretations, and conclusions are my own and should not be attributed to the World Bank, its Executive Board of Directors, or any of its member countries. I thank Jehan Arulpragasam, Deepak Bhattasali, Shubham Chaudhuri, Karl Kendrick Tiu Chua, Sanjay Dhar, Indermit S. Gill, Homi Kharas, Antonio M. Ollero, Lant Pritchett, Jamele Rigolini, and Vera Songwe for their inputs and comments. The usual disclaimers apply.
## ESTIMATION AND RESULTS

The cross-entropy estimation method is technically compatible with any CGE model. In the implementation, we choose a widely used standard, the IFPRI model (Lofgren, H., et al. 2002) as recently implemented by Cicowiez, M. and H. Lofgren (2006). This is a static CGE model for a single open economy in the tradition of Dervis, K., et al. (1982) and De Melo, J. and S. Robinson (1989). Over the years, CGE models of this type have been applied to a wide range of analysis of economic policy and external shocks in developing countries; hence, it is a good starting point for illustrations; see a recent survey of CGE models with policy applications to developing countries by Devarajan, S. and S. Robinson (2013). Since the specifications of the standard IFPRI CGE model are well documented, we only briefly sketch its outline to emphasize the key unobserved parameters of behavioral relationships for estimation. figure 1 of appendix 4 depicts the general structure of the production and consumption sides in the model, respectively. For illustrations of the methodology, we select Korea as a country case and Sub-Saharan Africa as a regional application.

# SAM preparation

Although there are good and extensive data available in the case of Korea, we do not include all of them in order to demonstrate parameter estimation in more limited data circumstances that are likely to prevail in other developing countries. Accordingly, we constructed five specific SAMs of Korea for the following years, 1990, 1995, 2000, 2005, and 2011. We aggregate the SAMs to cover 6 sectors – agriculture, mining, manufacturing, utilities, construction, and services.

As described in the data step, we generate SAM cells error distributions. We assign to the SAM total expenditure and total income cells an error support that covers three standard deviations of the correspondent prior SAM cell value. For the macro aggregates, we generate error distributions with smaller standard deviations that account for only 0.01 the macro aggregate prior mean value, i.e., the symmetric macro aggregate error supports cover a range ±0.03 the macro aggregate prior mean values. Table 1 illustrates how the Korean macro results deviate (in percent) from the numbers of the national accounts.

Table 2 shows a similar application at a regional level, the case of Sub-Saharan Africa, using the regional SAMs for 2004, 2007, and 2011 from the GTAP database. The GTAP SAMs are already expressed in U.S. dollars, which is convenient for the data step since regional income accounts are available in the World Development Indicators at the World Bank in both current and constant price series. This means relevant price indices are derivable for regional GDP and for several of its components. The data step in this case consists of deflating the SAM values by first using the regional U.S. GDP deflator and then 13
**Table 6. Cross section 1971-1995, OLS.**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||OLS|OLS|OLS|OLS|OLS|OLS|
||pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|
|Constant|-2.528|-3.061|-3.657|-3.528|-2.665|-2.486|
||(0.970)**|(1.161)**|(1.239)***|(0.996)***|(1.265)**|(2.058)|
|GDPpc71|-0.001|-0.000|-0.000|-0.000|-0.001|-0.001|
||(0.000)***|(0.000)|(0.000)|(0.000)|(0.000)***|(0.000)***|
|prim_expen_70|-0.053|-0.028|-0.058|-0.041|-0.060|-0.025|
||(0.034)|(0.039)|(0.065)|(0.036)|(0.037)|(0.058)|
|second_expen_70|0.002|0.001|-0.000|0.001|0.002|-0.013|
||(0.001)|(0.001)|(0.004)|(0.002)|(0.002)|(0.009)|
|Invt_GDP7195|0.227|0.223|0.289|0.270|0.234|0.220|
||(0.042)***|(0.054)***|(0.054)***|(0.042)***|(0.053)***|(0.074)***|
|main7195|0.022||||0.022||
||(0.003)***||||(0.004)***||
|egc7195||2.871||||4.108|
|||(2.084)||||(2.577)|
|rail7195|||0.127||||
||||(1.687)||||
|pavroads7195||||-0.172|||
|||||(0.362)|||
|tel_faults7195|||||0.001||
||||||(0.003)||
|elec_loss7195||||||0.030|
|||||||(0.071)|
|Observations|51|48|41|51|47|33|
|R-squared|0.68|0.53|0.50|0.56|0.69|0.50|

Robust standard errors in parentheses. Significant at 10%; ** significant at 5%; *** significant at 1%.

26
<!-- Start of picture text --> 9 Bibliography<br>References<br><!-- End of picture text --> <!-- Start of picture text -->
[1] Dinopoulos, Elias and Mordechai E. Kreinin [1988], "Effects of the U.S.-Japan Auto VER on<br>European Prices and U.S. Welfare," Review of Economics and Statistics, vol.70, p.484-91.<br>(2) Crandall, R. [1985], "Assessing the Impact of the Automobile Voluntary Export Restraints<br>upon US Automobile Prices," The Brookings Institution.<br>[3] Dypski, Michael Cornell [2002], "The Caribbean Basin Initiative: An Examination of Struc-<br>tural Dependency, Good Neighbor Relations, and American Investment," Journal of Transna-<br>tional Law and Policy, Volume 12(1).<br>[4] Evans, Carolyn and James Harrigan [2002]. "Distance, Time and Specialization," Federal Re-<br>serve Bank of New York, Working Paper.<br>[5] Feenstra , Robert C. [1989], "Automobile Prices and Protection: The U.S. Japan Trade Re-<br>straint," Journal of Policy Modelling vol. 7(1-2), p.49-68.<br>[6] Feenstra , Robert C. [1989], "Symmetric Pass-through of Tariffs and Exchange Rates under<br>Imperfect Competition: An Empirical Test," Journal of International Economics vol. 27, p.25-<br>45.<br><!-- End of picture text -->

[7] Goldberg, Pinelopi Koujianou and Knetter, Michael M. [1997], "Goods Prices and Exchange Rates: What Have We Learned?," Journal of Economic Literature, v35, p.1243-72. [8] Hoekman, Bernard, Constantine Michalopoulos and L. Alan Winters [2003], "Development and More Favorable and Differential Treatment of Developing Countries," mimeo, World Bank, Washington D.C.

[9] Kreinin, Mordechai E. [1961], "Effect of Tariff Changes on the Prices and Volume of Imports," American Economic Review, vol. 51, p.310-324 <!-- Start of picture text --> 29<br><!-- End of picture text -->