rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 5.2 Hazard -Survival function - unemployment by gender no CI.pdf<br>Theresex145Data0.0023MonthsBHPSSurviv05725is=isaFemaleMale-clearrightspentWdiscontil vesfunctioncensoredinuity1-16unemploymentin months- -all10Uandspells11mployment(beforendtheatfirstwavered line)16.Spells; and in month 23 (before the second red line).<br>Survival function - Unemployment Spells<br>BHPS - Waves 1-16<br>0 10 20 30 40 50<br>Months spent in unemployment<br>sex = Female sex = Male<br>There is a clear discontinuity in months 10 and 11 (before the first red line) ; and in month 23 (before the second red line).<br>Data is right censored - all spells end at wave 16.<br>1.00<br>0.75<br>0.50<br>0.25<br>0.00<br><!-- End of picture text --> does not support the hypothesis of habituation, when using the GSOEP and the ECHP<sup>17</sup> , but the results are not significant using the BHPS. The same exercise is executed here.

Using pooled data, the interaction term between unemployment and its duration, it is found that unemployment duration has a positive and significant effect on well-being, when one does not control for the other life variables. However, if one controls for life variables, then duration has no effect on well-being. This hints that duration is associated with the life variables mentioned above, but not directly with well-being. The transition matrix shown before provides evidence that staying in unemployment has no different effect on well-being than staying in employment. As pooled data is not rigourous, panel data specifications are used. Introducing individual fixed effects makes it possible to regress the change in well-being to the change in duration. A fixed effect logit is used to estimate this effect of duration on well-being, for the unemployed. The results show that the coefficient of duration is not significant. Hence, it can be said that the effect of unemployment on well-being is independent from its duration. These results are shown in the appendix. Table A.8 shows the pooled results, and Table A.9 the fixed effects results.

> 17GSOEP is the German Socio-Economic Panel ; ECHP is the European Community Household Panel

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**Table 1 World trade shares, percent**

||Agricul|ture|Processed|food|Fossil f|uels|Manufact|uring|Servi|ces|Tot|al|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||2005|2030|2005|2030|2005|2030|2005|2030|2005|2030|2005|2030|
|**Export shares**|||||||||||||
|China|10|1|11|17|1|0|11|21|1|0|9|16|
|India|1|0|1|2|0|0|1|3|1|0|1|2|
|East Asia ex. China|4|2|8|12|6|7|6|10|2|0|6|8|
|Eastern Europe|4|1|6|9|13|14|6|8|4|1|6|7|
|Latin America|15|10|12|14|11|13|6|8|3|0|6|7|
|Middle East|1|1|2|1|39|38|3|6|3|1|4|5|
|South Asia ex. India|1|1|1|1|0|0|1|1|0|0|0|1|
|Sub-Sah. Africa|3|2|3|3|9|12|1|2|1|0|1|2|
|Developing|38|19|43|60|79|84|34|60|15|2|33|48|
|High income|62|81|57|40|21|16|66|40|85|98|67|52|
|**Import shares**|||||||||||||
|China|24|45|1|1|4|12|8|8|5|14|7|11|
|India|1|1|1|1|4|6|1|1|1|2|1|1|
|East Asia ex. China|4|4|6|6|3|4|4|6|5|8|4|6|
|Eastern Europe|8|11|8|7|9|12|6|6|5|8|6|7|
|Latin America|7|8|6|5|3|3|6|7|6|9|6|7|
|Middle East|6|6|5|7|1|1|4|4|5|8|4|5|
|South Asia ex. India|1|0|1|1|0|0|0|1|1|1|0|1|
|Sub-Sah. Africa|2|2|2|2|1|1|1|1|2|4|2|2|
|Developing|53|77|30|31|26|39|30|35|30|54|31|40|
|High income|47|23|70|69|74|61|70|65|70|46|69|60|

The last two columns of Table 1 show that the exports of low and middle income countries could account for one-half of total global exports by 2030, up from one-third in 2005. This expansion is driven to a large extent by the growth in trade originating in China and other Asian countries, with the former already the world’s second leading exporter in 2005 behind high income European countries. This growing importance of developing countries is also reflected in changes in the direction of trade, which is also likely to undergo significant shifts over the next 25 years. Figure 9 and Figure 10 show that the faster pace of growth in developing countries translates into their rise as destinations for Chinese and Indian exports. At the same time, China and India buy many more of their manufacturing imports from developing countries, while agricultural imports are increasingly sourced from high income countries (consistent with the analysis above). Much of the growing trade dependence between developing countries is due to trade in intermediate goods—today, 63 percent of China’s imports are classified as intermediate goods, with roughly half of them coming in the form of parts and components. With the increasing orientation of developing countries towards manufacturing sectors, these linkages are likely to become even stronger in 2030.

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Anecdotal reports suggest that this problem is more severe at private providers because they must worry about covering staff costs as well as medicines, supplies, and diagnostic tests.

Ghanaians who are not covered by NHIS must pay for healthcare on a fee-for-service basis. However, as we noted above, at public healthcare facilities, salaries and capital expenditures are covered by the central government budget, so the fees cover only nonsalary, noncapital costs.

# **3 Calculating Out-of-Pocket Healthcare Expenditures**

GLSS 2016/17 asks about healthcare expenditures for each individual if in the past two weeks she or he consulted a healthcare provider, purchased medicines, or was admitted to a hospital. For outpatient services, survey respondents can report what they paid for registration at the healthcare facility, consultation, diagnosis (laboratory fees, etc.), and medicines, or they can report the total amount they paid.<sup>12</sup> In our calculations, we use the larger of the sum of the individual items or the reported total. For inpatient services, survey respondents only report what they paid for “staying in a hospital or health facility.”<sup>13</sup> Survey respondents can also report “medicines and medical supplies” purchased outside the context of a healthcare visit.<sup>14</sup> Finally, they can report “total medical expenses.”<sup>15</sup> To calculate total OOP healthcare expenditures, we add the total of outpatient expenses, inpatient expenses, and medicine expenses and take the larger of this sum or the “total medical expenses” reported. In a few cases, respondents report exactly the same amount for medicines acquired in the context of a consultation and medicines acquired without a consultation.<sup>16</sup> For these, we set the value for OOP healthcare expenditures outside the context of a consultation to zero to avoid double counting these expenditures.

All the raw variables in GLSS 2016/17 have some very large positive outliers. For each of the raw variables and again for our calculated total OOP healthcare expenditures, we replace any value that is larger than the respondent’s household consumption (not per capita) or five

> 12 These are questions 9 through 12 and question 13 in Section 3A of the GLSS 2016/17 questionnaire. We have excluded question 14 (“any other services”) and question 15 (“travel”) from our total out-of-pocket healthcare expenditures as these are not healthcare expenditures per se.

> 13 Question 20 in Section 3A of the GLSS 2016/17 questionnaire.

> 14 Question 22 in Section 3A of the GLSS 2016/17 questionnaire.

> 15 Question 23 in Section 3A of the GLSS 2016/17 questionnaire.

> 16 Questions 12 and 22 in Section 3A of the GLSS 2016/17 questionnaire, respectively.

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indicators are available, while in panel (b) we show the average regional trend over the 1997-2015 period.<sup>13</sup> _Figure 4. Unfairness perceptions and Gini coefficient in Latin America_

- _a) Across countries (Pooling all the countries and years)_

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40 Unfairness = 91.35 Gini + 33.4<br>R2 = 0.15<br>30<br>0.35 0.40 0.45 0.50 0.55 0.60<br>Gini<br>% Unfair or Very Unfair<br><!-- End of picture text -->

- _b) Over time (Cross-country average, 1997-2015)_

<!-- Start of picture text --> 100 0.60<br>90 0.55<br>Gini coefficient (RHS)<br>80 0.50<br>% Unfair or very unfair (LHS)<br>70 0.45<br>60 0.40<br>1997 2000 2005 2010 2015<br><!-- End of picture text --> Note: Panel (a) of this figure presents the cross-county correlation between unfairness perceptions and the Gini coefficient for 18 LA countries over the 1997-2015 period. The figure does not include data points that 13 In 1997 Latinobarómetro had a low coverage in large countries with relatively high levels of inequality (such as Brazil and Colombia), and did not survey other countries at all (such as Dominican Republic). The increase in the coverage of the survey could drive part of the change in perceptions between 1997 and 2001.

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We would expect TIMSS and PISA results to vary little after transformation since they are already on an international scale. Table 8.0 quantifies this change. We see that TIMSS scores change less than a single point on average. While PISA math and science scores are underestimated by our HLO by around 20 points, this is a relatively small difference, and for reading scores there is essentially no difference between PISA scores and our HLO outcomes. This indicates a relatively high degree of robustness in comparison to raw ISAT scores.

We expect scores for developing countries to change in our HLO database since indeed this is the purpose of the linking function. However, ranks should be preserved relative to their original rank within each group of countries that participated in the original RSAT used in the anchoring process. We verify whether this assumption holds.

Although we see RSAT scores change significantly, especially for SACMEQ, ranks remain stable. The average rank change in math for SACMEQ countries is 0, with a standard deviation of .2 for math scores; LLECE countries’ math scores are slightly more sensitive to rank changes, with an average rank change of 0 and a standard deviation of 1. However, in both instances, original RSAT ranks are consistent with our HLO ranks, indicating a high degree of robustness. Qualitatively, we see that the first SACMEQ has no change in ranks, and SACMEQ II has one change in ranks with a one-rank swap between Tanzania and Seychelles. For SERCE there are a few rank shifts ranging from shifts of one to five. In TERCE there are only two shifts of 1- rank swaps between Nicaragua and Panama and Colombia and Ecuador. We conduct a similar exercise across TIMSS primary and secondary math scores from 1995-2015, as well as PISA secondary reading scores from 2000-2015. We find an overall average rank changes of 0 for all assessments, with standard deviations ranging from 1.4-2.5. If you include RSATs, the average rank change remains 0, and the average standard deviation shift is 1.4. Table 8.1 and Figures 15.0-15.16 show these results in depth. Additional results are available on request.

Overall, we find that our HLO database while more expansive, produces similar results to both raw scores, ranks and IRT-equated scores where there is overlap. This increases our confidence in the robustness of our estimates. We also provide standard errors to provide an added measure of reliability by quantifying the uncertainty around our methodology and estimates.

A future robustness check would be the inclusion of psychometric adjustments. Jerrim et al. (2017) highlight a few features of ISATs that economists often ignore. Jerrim et al. (2017) conclude that results of a paper they re-analyze by Lavy (2015) are robust to inclusion of these 42
**Table 1: Estimation of the NEG wages equations for the urban and rural sectors, 2007**

|||**Urban**<br>|||**Rural**<br>||
|---|---|---|---|---|---|---|
|**Variable**|**OLS**|**2SLS**|**FGS2SLS**|**OLS**|**2SLS**|**FGS2SLS**|
|Constant|3.042***<br>(3.533)|5.032***<br>(4.219)|3.911***<br>(3.892)|3.471***<br>(11.618)|3.564***<br>(11.332)|3.091***<br>(11.091)|
|ln(_human_)|0.565***<br>(7.744)|0.602***<br>(7.985)|0.597***<br>(8.135)|0.608***<br>(5.854)|0.431**<br>(2.379)|0.407**<br>(2.192)|
|ln(_I pw_)|0.107***<br>(5.637)|0.142***<br>(4.149)|0.112***<br>(5.876)|0.0936***<br>(7.877)|0.093***<br>(7.614)|0.090***<br>(7.488)|
|**W***ln(_human_)|-0.335***<br>(-2.602)|-0.281**<br>(-2.125)|-0.279**<br>-2.113|0.566***<br>(3.437)|0.530***<br>(3.105)|0.515***<br>(3.005)|
|**W***ln(_I pw_)|0.148***<br>(4.400)|0.142***<br>(4.149)|0.142***<br>(4.102)|0.0946***<br>(4.662)|0.086***<br>(3.899)|0.077***<br>(3.440)|
|ln(_RMAdom_)|0.357***<br>(4.406)|0.136<br>(1.119)|0.156<br>(1.211)|0.139***<br>(8.274)|0.199***<br>(3.814)|0.205***<br>(3.770)|
|ln(1 +<br>45<br>.0<br>_port_<br>_t_<br>)|-0.190***<br>(-3.664)|-0.251***<br>(-4.316)|-0.226***<br>(-3.820)|-|-|-|
|<sup>ˆ </sup>/ ˆ|-|-|0.195***<br>(6.890)|-|-|0.164***<br>(7.416)|
|ˆ /ˆ|2.801|7.332|6.424|7.170|5.031|4.887|
|2<br>_R_|0.766|0.760|0.771|0.859|0.853|0.856|
|2<br>_R_|0.739|0.732|0.744|0.842|0.836|0.839|
|2<br>ˆ/<br>2ˆ|0.017|0.017|0.015|0.026|0.028|0.024|
|F1st stage|-|121.567***|121.567***|-|18.038***|18.038***|
|||(p = 0.000)|(p = 0.000)||(p = 0.00)|(p = 0.000)|
|Sargan,<br>)1(<br>2<br>|-|0.077<br>(p = 0.781)|0.005<br>(p = 0.946)|-|0.208<br>(p = 0.649)|0.341<br>(p = 0.560)|
|Spatial_z_-ratio|6.368***|3.329***|-|5.444***|2.626***|-|
|,|(p =0.000)|(p = 0.001)||(p =0.000)|(p = 0.009)||

**Notes:**

***: significant at the 1 % level; **: at the 5 % level Numbers in parenthesis under parameter estimates are t-ratios

_F1st stage_ is a F-test for the joint significance of the instruments in the 1<sup>st</sup> -stage 2SLS regression. For urban, this test has (2, 294) degrees of freedom; for rural, it has (2, 295) degrees of freedom _Instruments:_ urban – ln[  j{1/[areaj(1+dij)]}], **W** * **W** *ln( _I pw_ ); rural - **W** * **W** *ln( _I pw_ ), **W** * **W** *ln( _human_ ), ln(1+diport0.45) _Sargan_ - statistic for Sargan's test for validity of instruments _Spatial_ - statistic for Moran I's test for spatially autocorrelated errors (OLS) or Anselin-Kelejian (1997) test for spatially autocorrelated errors in the presence of endogenous regressors (2SLS) The 29 Provincial dummy <u>coefficients are of limited interest and have been omitted to save space.</u> 23
3 countries, where quota prices are harder to come by, researchers often use Hong Kong quota prices as proxies.<sup>7</sup> However, simply assuming the standard competitive model as the basis for empirical analysis of the MFA may be a somewhat cavalier approach. The underlying assumption in the standard competitive model is that both the demand and supply sides of the product market are competitive and, in addition, license holders act competitively and are willing and able to sell at the price that clears the license market. A host of possible alternatives to the competitive paradigm is possible, and many of these have not been analyzed. If imperfections exist, then institutional details such as the quota implementation procedure and license allocation mechanism in the exporting country become more important considerations. If market imperfections result in rent sharing, as we argue here, existing estimates of the effects of liberalization will change dramatically.

The assumption of competitive markets in the study of the MFA is usually defended on the grounds that there are a large number of producers i.-i the textile and apparel market. In the case of some advanced exporters, notably Hong Kong, further justification is provided by the fact that the quotas are efficiently implemented and are, to a large extent, transferable. However, while it seems reasonable to assume that Hong Kong producers are competitive, it is not clear that market power does not exist in the market for quota licenses and on the side of the US purchasers. For example, the 1988 numbers equivalent of the Herfindahl index of concentration in license holding was 3.95 for MFA category 340 (men's and boys cotton woven shirts), and 5.49 for MFA category 434 (men' s woollen coats and jackets). The five-firm concentration ratio in license holdings for these two categories was 83 per cent. An editorial in the Hong Kong trade journal, Textile Asia, alleges that: "Quota price fluctuations do not in fact reflect normal supply and demand but the course of manipulation by the quota holders"<sup>8</sup> while Goto (1989, p.218) claims that: "Although governments of exporting countries' under the MFA often allocate export licenses in a manner that helps exporters capture the quota rent, many of these exporters face large importing enterprises that can negotiate prices that capture some of the rent for themselves." For the
- Freund, Caroline F. and Diana Weinhold. 2004. “The Effect of the Internet on International Trade,” _Journal of Economics_ Vol. 62 No. 1 (January), 171‐189.

Ghani, Ejaz, ed. 2010. _The Service Revolution in South Asia_ . New York: Oxford University Press.

Gibbs, J. Murray. 1986. “Services, Development, and TNCs.” _CTC Reporter_ 21 (Spring): 51–53.

- Goswami, Arti Grover, Aaditya Mattoo, and Sebastián Sáez, eds. 2012. _Exporting Services: A Developing Country Perspective._ Washington, DC: World Bank.

- Hausmann, Ricardo, Jason Hwang, and Dani Rodrik. 2007.  “What You Export Matters.” _Journal of Economic Growth_ 12(1): 1–25.

- Inklaar, R., Timmer, M., & Ark, B. v. 2007. “Mind the Gap! International Comparisons of Productivity in Services and Goods Production.” _German Economic Review_ 8 (5): 281–307.

- ——. 2008. “Market Services Productivity across Europe and the US.” _Economic Policy_ 23: 141–94.

- Jensen, Jesper, Thomas F. Rutherford, and David G. Tarr. 2004. “The Impact of Liberalizing Barriers to Foreign Direct Investment in Services: The Case of Russian Accession to the World Trade Organization.” Policy and Research Working Paper No. 3391, World Bank, Washington, DC.

- ——. 2005. “Telecommunications Reform within Russia’s Accession to the World Trade Organization,” Policy Research Working Paper 3501, World Bank, Washington, DC.

- Jensen, Bradford. 2008. “Trade in High‐Tech Services.” _Journal of Industry, Competition, and Trade_ 8 (3– 4): 181–97.

- Lennon, Carolina & Mirza, Daniel & Nicoletti, Giuseppe (2009) “Complementarity of Inputs in Services Trade”, Annales d’Economie et de Statistiques, Number 93/94, April/June.

- Lennon, Carolina (2008) “Trade in Services: Cross‐Border Trade vs. Commercial Presence: Evidence of Complementarity”, WIIW Working Paper No 2008‐53 59, Vienna: WIIW.

- Lennon, Carolina. 2009. “Trade in Services and Trade in Goods: Differences and Complementarities.” The Vienna Institute of International Economics Studies, Working Papers 53.

- Mankiw, N. Gregory, Paul Romer, and David N. Weil. 1992. “A Contribution to the Empirics of Economic Growth.” _Quarterly Journal of Economics_ 107: 407–37.

- Nusbaumer, Jacques. 1987. _Services in the Global Market_ . Amsterdam: Kluwer Academic Publishers.

- Miroudot, Sébastien & Sauvage, Jehan & Shepherd, Ben (2010). "Measuring the Cost of International Trade in Services," MPRA Paper 27655, University Library of Munich, Germany.

- Sáez, S, D. Taglioni, E. Van der Marel, and V. Zavacka. 2014. Services Trade Competitiveness Diagnostic Toolkit, International Trade Unit, World Bank, Washington, DC, unpublished.

- Tarr, D.G. (2012). Putting Services and Foreign Direct Investment with Endogenous Productivity Effects in Computable General Equilibrium Models, Policy Research Working Paper 6012

- Triplett, Jack E. and Barry P. Bosworth. 2004. _Services Productivity in the United States:New Sources of Economic Growth_ . Washington, D.C.: Brookings Institution Press.

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public investment. Roubini and Sachs (1989) observe that public investment typically responds sharply to restrictive fiscal policies in OECD countries – and much more than current expenditure. Similarly, the _World Development Report_ of 1988 reported that cuts in public investment were on average three times greater than cuts in current expenditures during fiscal adjustment exercises during the 1980s. Lane (2002) finds that government investment is the most cyclical component of government spending.

Likewise, fiscal adjustments have had discernable negative impacts on public investment in Latin American countries. Calderon, Easterly, and Serven (2003) argue that about half of the fiscal adjustment in LAC during the 1990s was achieved through cuts to infrastructure investment.

Gali and Perotti (2003) offer a detailed examination of the effects of the SGP on investment in the euro zone. They find that government investment as a share of potential GDP fell in the euro zone by 0.47 percentage points on average following implementation of the Maastricht Treaty. But investment also fell by comparable amounts in a comparison group of other EU and OECD countries. Thus, while there is a clear downward trend in public investment in developed countries, it is not unique to the SGP. They further argue that the downward trend substantially predates the Maastricht Treaty: the investment decline from 1978 to 1992 was of a similar magnitude to that of the later period in both the euro zone countries and the OECD comparison group.

As argued above, fiscal rules often have stronger cyclical compared to trend effects on public investment. Gali and Perotti, however, argue that, while public investment expenditures are pro-cyclical in the euro zone, there is no evidence that cyclical behaviour has changed since implementation of the Maastricht rules.

As well, trends in public investment in Europe and elsewhere may reflect accounting and institutional changes, in addition to fundamental changes in policy. In particular, changes in the treatment of public utilities, the effects of privatization, and the development of public-private partnerships for infrastructure may be reflected in the public accounts. For the United Kingdom, Balassone and Franco (2000) suggest that up to one-third of the decline in public investment may be attributed to changes in project financing regimes.

# **<u>5. Recent proposals for reform</u>**

As outlined in section 2.1, two approaches have been suggested to provide greater incentives for public investment in the presence of fiscal rules related to debt constraints: the Golden Rule and Permanent Balance Rule.

22