rafmacalaba/datause-encoder-data huggingface dataset:
appear to have adjusted fast enough to reflect the narrowing gap in infrastructure quality.<sup>6</sup> Compounding the problem is the global trend of increased commoditization of many manufacturing products made possible by technological advances, which favor countries with low operating costs. These developments have led economists to conclude that, to remain internationally competitive, the Singapore economy has to be more innovative, with the capability to develop its own brands of goods and services. Efficiency alone will not guarantee sustained robust growth in the future.<sup>7</sup> Debates on creativity and innovation are not new in Singapore, although the strategic focus has shifted over the years. Krugman (1994) and Young (1992, 1995), for example, questioned the basis and sustainability of Singapore’s economic growth in a series of studies as far back as the early 1990s. Pointing to the low contribution of total factor productivity (TFP) growth, Krugman referred to the Singapore miracle as having been based on “perspiration rather than inspiration”: “Singapore grew through a mobilization of resources that would have done Stalin proud.... All of Singapore’s growth can be explained by increases in measured inputs. There is no sign of increased efficiency.” While disputing such views,<sup>8</sup> the Singapore government has since set aside more resources to promote R&D and innovation. Among the more visible efforts were the various five-year plans laid out by the National Science and Technology Board (NSTB), starting from the early 1990s. These efforts were however targeted mainly (and narrowly) at short-term, applied technological innovations, with few attempts to deepen the culture and practice of innovation across the whole economy.

Success stories have been sporadic so far. As Wong (2003) observed, “While Singapore’s technology deployment capabilities (to operate and adapt technologies) are now close to the world frontier, its capability to create technologies (to innovate and pioneer new technologies) is still lagging considerably behind this frontier.”<sup>9</sup>

> 6  The government has over the past year begun to tackle the labor cost by reducing the compulsory contribution rates to the pension funds by employers and to inject more flexibility into the wage setting system.   However, it is a lot more cautious in its moves to reduce the land costs for fear of sparking a severe plunge in the property markets. 7   See various studies on this issue by the World Bank, including Yusuf et al, 2003.

> 8   See Rao and Lee, 1996 for various views on this issue.

> 9 See Wong, 2003, page 12.  As an indication of Singapore’s technology deployment capabilities, the World Economic Forum Global Competitiveness Report 2001-2002 ranks Singapore top in networked readiness.  In the area of telecommunications connectivity, besides a nationwide broadband network infrastructure, Singapore has 21 Tbps of installed international capacity, sufficient for `every business need’.  See <u>www.one-north.com.</u>

2
income is ignored (so that gross earnings are netted out). Instead of an impact of BREGS earnings of a 1.4% point drop in the poverty rate in R1 (allowing for forgone income), we would have obtained an estimate of 1.7% points. The difference is greater in R2, for which the poverty impact would have been 1.4% points based on gross earnings, as compared to 0.5% points when factoring in forgone earnings. So even if we have substantially over-estimated forgone incomes using the individual survey responses, the poverty impacts for the rural population as a whole are not substantially greater.

The above calculations assess impact relative to the absence of the program as the counterfactual state. It is of greater interest to assess impact relative to a feasible alternative allocation of the same total expenditure. The simplest alternative is to give every household (whether poor or not) the same sum of money as was spent on BREGS—a uniform transfer, as discussed in the Introduction. Note (again) that this comparison does not take account of the asset creation under BREGS. The question posed here is whether the wage earnings gains alone can justify the scheme as a cost-effective means of reducing poverty.

Using the same methods described above, we estimate that such a basic-income scheme would reduce the poverty rate from 62.2% to 60.8% among BREGS participants in R1, and from 52.6% to 50.9% in R2. Both resulting poverty rates are higher than under BREGS. Of course, for a BIS the more relevant comparison is for the population as a whole. We find that a BIS would reduce the overall poverty rate from 51.4% to 49.5% in R1, while in R2 it would reduce it from 42.3% to 39.1%. In this case, both are lower than under BREGS (Table 7).

This simulation assumes that there is no leakage of money from the budget for the transfers, which is unrealistic. If we make a (seemingly generous) allowance for leakage in these transfers, namely that 10% is lost, then the poverty rates fall to 49.7% and 39.6% in R1 and R2 respectively for the population as a whole—still an improvement over BREGS.<sup>28</sup> Another simulation of interest is to once again take the same budget but instead give it out equally (in cash or kind) to all households holding a BPL ration card. Of course this is feasible since the BPL cards are already issued by the government, and there is an existing infrastructure of ration shops for the subsidized food provided under this policy. We assume that the government only knows who has a BPL card; it does not know who is really poor and how poor

> 28 The results are not very different if we use the estimated leakage in BREGS (assuming the discrepancy between survey responses and administrative data is due to leakage to unintended beneficiaries). Poverty rates fall to 49.6% and 39.2% in R1 and R2 respectively for the uniform transfer.

22
<!-- Start of picture text --> CZ EE UA SK RU LV BG HU PL RO SI DK FI NO SE CY IL GR PT ES IT BE IE DE UK AT FR NL CH<br>East North South West<br><!-- End of picture text --> <!-- Start of picture text --> CZ EE UA SK RU LV BG HU PL RO SI DK FI NO SE CY IL GR PT ES IT BE IE DE UK AT FR NL<br>East North South West<br>Employees with contracts current Employees with contracts past<br>Employees without contracts total Employees without contracts current<br>Informal self-employed total<br><!-- End of picture text -->

**Figure 16 Trade-union experience of formal and informal workers, by country. 2004-2009**

_Note:_ The sample size of employees without contracts is less than 30 for IT, SE and FI; between 50 and 90 for CZ, RO, SK, HU, EE, BE, FR, DE and CH; between 100 and 745 for other countries. _Source:_ Calculation with ESS data By contrast, in Hungary, Romania, Slovenia, Spain, Italy, Netherlands, and Switzerland proportions of informal employees with union experience are low both in absolute terms and in comparison with formal workers, suggesting substantial segmentation. It is more difficult to interpret situation in remaining Eastern European countries (the Czech R., Slovakia, Poland, Estonia, Latvia, Bulgaria, Ukraine, and Russia), where the proportions of informal workers (both employees and self-employed) with union experience are substantial but it is not clear whether this experience refers to the socialist era or to the market economy period.

# **3.3 Are informal workers poorer than the formal ones?**

Figure 17 compares perception of household financial situation by formal and informal employees. In both the Eastern and Southern Europe, the proportion of those seeing their situation as ̳very difficult‘ is much higher among non-contracted workers: 19% vs. 10% in the East and 11% vs. 4% in the South<sup>22</sup> . The situation is similar when respondents describing their situation as ̳difficult‘ or ̳very difficult‘ are taken together: This category accounts for 53% of informal employees and just 41% of formal employees in the East, while

> 22 These (rounded) figures are obtained after excluding non-response which for the question at hand was quite small. Recall that countries are not weighted by population size.

36
**Table 4. R-squared Shapley–Owen Decomposition for Program-Level Contributions**

|Graduation|Formal<br>Employment|Wages<br>(log)|
|---|---|---|
|(1)|(2)|(3)|
|_Percent of explained variation attributable to:_|||
|Institution<br>93.82|79.32|75.74|
|Field of study<br>6.18|20.68|24.26|
|R-squared<br>0.62|0.43|0.36|
|Observations<br>621|851|851|

_Source_ : Authors’ calculations using administrative data. _Notes_ : This table presents the results from the R-squared Shapley-Owen decomposition for the regressions of program-level contributions to institution fixed effects and field fixed effects (one regression per outcome). For each regression, the table shows the R-squared and the percent of the explained variation attributable to each set of fixed effects. “Observations” indicates number of programs (equal to the number of observations in the underlying regression).

42
telphone companies outpaid investments in compefing domestic companies. Retums on investment in forign and overseas twnways outstripped those on domestic tram companies after 1876. Much of this diffeece is plausibly attributable to the greater riskiness of foreign and overseas investment, as Edelstein demonstrates. But for investors able to diversify away the unsystematic risk assciated with pardcular foreign invesments (rough reliance on investmen **t** msts, for example), t foreign equity premium clearly sufficed.

Table 7. Brilish Overseas hnvstnemt i. Publidy-isseud Securifies, Deemuber 1913

||_£millon_|Percent|
|---|---|---|
|Goemmet<br>amd mmicipa|1,125.0|29.9|
|Dominion and coltnia govemnuIs|675.5|17.9|
|Foragp goverant|297.0|7.9|
|Oveuseau micipaliiies|152.5|4.1|
|Rawaya|1,531.0|40.6|
|<br>Domio<br>amd colonia xaaiwyw|306.4|8.1|
|<br>Indian railways|140.8|3.7|
|United State mailways|616.6|16.4|
|Other fbreig naifay|467.2|12.4|
|Oter public utilities|185.1|_5.0_|
|Elctric liHt and power|27.3|0.7|
|Gas and wateworks|29.2|0.8|
|Cans ad dock|7.1|0.2|
|Tramways|77.8|2.1|
|Telegpbs and tephon|43.7|1.2|
|Commerce ad induy|208.5|_5.5_|
|<br>CoaL imo and ted|_35.2_|0.9|
|Breweries|18.0|_0.5_|
|Other conuercia and industra|155.3|4.1|
|RaW Materas<br>|388.5<br>|10.3|
|Mines|_m.2_|7.2|
|Nitrat|11.7|0.3|
|Oil|40.6|1.1|
|Rubber|41.0|1.1|
|Tea ad coffe|22.4|_0.6_|
|Bsm and ince|317.1|8.4|
|Banks|72.9|1.9|
|Fi_ancial, lad amd investmt|244.2|_6.5_|
|Mlilnenam|8.1|0.3|
|Total|3,763.3|100.0|

Sowm Thom 1967, p.<sup>1 4</sup> .

40
# Table A.6: Social integration of Albanian immigrants arrived in Greece at different age

|Albania-born arrived in Greece when|Young (_<_24)|Older(_≥_24)|Difference|
|---|---|---|---|
||(1)|(2)|(1)-(2)|
|Greek citizen|0.149|0.111|0.038|
||(0.044)|(0.033)|(0.054)|
|Ever feel discriminated against|0.522|0.489|0.033|
||(0.061)|(0.053)|(0.081)|
|Greek as language most often spoken at home|0.507|0.567|-0.059|
||(0.062)|(0.053)|(0.081)|
|Political activism (petition, boycott, demonstration...)|0.045|0.056|-0.011|
||(0.026)|(0.025)|(0.036)|
|Participate in civic organisation|0.077|0.080|-0.003|
||(0.077)|(0.055)|(0.095)|
|N|67|90|157|

This table shows average attitudes and behaviors of Albanian immigrants arrived in Greece at different age. Standard errors in parentheses. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1; Source: Author’s elaboration on the European Social Survey (ESS).

# Table A.7: Robustness check: Sport clubs and share of refugees in 1923

|Dep. var:||Presence o|f at least o|ne sport clu|b||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Share of refugees in 1923|0.066|0.120*|0.078|-0.015|0.039|0.023|
||(0.051)|(0.062)|(0.070)|(0.049)|(0.068)|(0.075)|
|Share of refugees in 1923 * small community||||0.266***<br>(0.100)|0.265***<br>(0.097)|0.187*<br>(0.100)|
|_R_<sup>2</sup>|0.288|0.295|0.327|0.288|0.295|0.328|
|Observations|4,393|4,393|4,393|4,393|4,393|4,393|
|Average outcome|0.712|0.712|0.712|0.712|0.712|0.712|
|Geography controls|Y|Y|Y|Y|Y|Y|
|Population 1991|Y|Y|Y|Y|Y|Y|
|Socio-Economic Development 1991|Y|Y|Y|Y|Y|Y|
|Region FE||Y|Y||Y|Y|
|Province FE|||Y|||Y|

**Notes** The table presents OLS regression estimates measuring the effect of the share of refugees in 1923 on the presence of sport club in the early 2000s, at the community level. Standard errors are clustered at the province level. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1; Source: Author’s elaboration on the 1928 population census, 1923 refugee census and General Secretariat of Sports’ inventory.

Table A.8: Sport clubs and Albanians immigrants

|Dep. var:||Presen|ce of at le|ast one spo|rt club||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Share of Albanian immigrants in 2001|0.123|0.135|0.131|0.134|0.141|0.050|
||(0.143)|(0.140)|(0.148)|(0.098)|(0.090)|(0.099)|
|Share of Albanian immigrants in 2001 * small community||||-0.015<br>(0.188)|-0.008<br>(0.185)|0.112<br>(0.188)|
|_R_<sup>2</sup>|0.288|0.295|0.328|0.288|0.295|0.328|
|Observations|4,393|4,393|4,393|4,393|4,393|4,393|
|Geography controls|Y|Y|Y|Y|Y|Y|
|Population 1991|Y|Y|Y|Y|Y|Y|
|Socio-Economic Development 1991|Y|Y|Y|Y|Y|Y|
|Region FE||Y|Y||Y|Y|
|Province FE|||Y|||Y|

**Notes** The table presents OLS regression estimates measuring the joint effect of the share of refugees in 1928 and the share of Albanian immigrants in 2001 on the presence of sport club in the early 2000s, at the community level . Standard errors are clustered at the province level. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1; Source: Author’s elaboration on the 1928 Greek population census and General Secretariat of Sports’ inventory.

37
and responsiveness of teachers toward the child, use of teaching strategies that encourage child participation, capacity for group and one-on-one interaction between teachers and children, supervision and disciplinary techniques) qualities of early learning centers.

Items were translated, back translated, reviewed, and adapted by Malawian early child development experts. Some items were also drawn from previous evaluations of CBCCs (Chiuye & Chimombo, 2011; Munthali et al., 2014). Our decision to use these measures, as opposed to other standardized Western tools such as the Early Childhood Environment Rating Scale (ECERS; (Clifford et al., 2010; Harms & Clifford, 1982)) and the Classroom Assessment Scoring System (CLASS; (Pianta et al., 2007)) was based on several factors. The CBCCs are unlike preschools and kindergartens for which the ECERS and CLASS were developed in that they are initiated, run and staffed by community volunteers with minimal education, training and oversight. Thus, the centers are not universal in form or function: they may operate erratically based on the availability of structures, teachers and materials; they tend to serve a wide range of ages, and the emphasis of preparing children for primary school may be secondary to other goals, such as providing a safe place for children to play, and get a meal. Our measures, informed by those developed in low- and middle-income countries, with added information from local experts, strove to measure quality in a contextually relevant manner. While these measures may not be externally valid (e.g., for comparing results across preschool interventions), they are useful for providing tools that can be used within Malawi for evaluating quality of centers. Our items might also be relevant to efforts at creating a universal measure of early learning environment quality.

We used the data collected under the observational tool to develop a _Classroom Observation Index_ with the purpose of measuring classroom quality. At each round, the index computes a weighted average of a set of underlying observational variables (31, 34, and 32 variables at baseline, 18-, and 36month follow-up, respectively) using the Inverse Covariance Weighting (ICW) methodology in Casey et al. (2012), which is described in detail under _Kaufman Assessment Battery-Children, 2_<sup>_nd_</sup> _Edition (KABCII)_ above.

63
Thus there are a large number of ways<sup>in which one can deriveplausible price changes</sup> resulting from policy reforms. However,<sup>theimpactof these price changesupon the poor</sup> depends on the nature of the household,<sup>to which we now turn.</sup> _The Nature of the Household_ The impact of a price change upon<sup>a household will clearly dependon the relative</sup> importance of different sources of<sup>incomeand of differentgoods inthe consumption</sup> basket. For example, if the price<sup>of a staplefood rises sharply then net producers will</sup> benefit, whereas net consumers will<sup>lose, but the extentof the gain or lossdepends upon</sup> how much income depends upon<sup>theproductionof this goodand how importantthisgood</sup> is in the household's consumption<sup>basket.Consequently,the best placeto start in</sup> determining the impact of a price shock<sup>is toobtain,for different groupsin society,</sup> information about the relative importance<sup>of different sourcesof income and the relative</sup> importance of different goods in<sup>householdconsumption.Table1 showsa typical table,</sup> from Cambodia, of sources of income<sup>for differentdecilesof the consumption</sup> distribution; Table 2 shows the expenditure<sup>shares,by decile of per capita consumption.</sup>

**Table 1: Contributions to income in Cambodia**<sup>**fromdifferent sources(percent)**</sup>

|_SourceofIncome:_<br>|_1_<br>|_2_<br>|_3_<br>|_4_<br>|_D_<br>_5_<br>|_eciles_<br>_6_<br>|_7_<br>|_8_<br>|_9_<br>|**_10_**<br>|_avg_<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Selfemployment|67.8|71.4|71.7|70.3|73.6|68.5|71.2|70.7|63.3|36.3|60.9|
|*fromcultivation|27.8|29.7|31.1|30.3|31.9|32.1|30.6|32.3|19.8|3.2|22.4|
|fromricecultivation|21.4|24.3|25.3|23.6|25.7|25.0|22.7|20.4|9.7|2.1|16.3|
|fromothercrops|6.4|5.4|5.8|6.7|6.2|7.1|7.9|11.9|10.1|1.1|6.1|
|*fromlivestock|16.6|14.3|14.2|13.0|12.4|11.9|11.6|12.0|8.3|1.5|9.4|
|*fromfishgrowing,etc|6.1|7.1|5.5|8.7|6.9|6.8|8.7|7.1|5.2|2.1|5.6|
|*fromforestryandhunting|8.3|10.2|9.7|7.3|7.7|7.9|8.1|6.1|4.9|0.6|5.7|
|*fromnonfarming<br>activites|8.9|10.0|11.2|10.9|14.7|9.8|12.2|13.2|25.1|29.0|17.7|
|Other sources|32.2|28.6|28.3|29.7|26.4|31.5|28.8|29.3|36.8|63.9|39.1|
|*fromwages|19.0|15.0|15.9|14.5|14.3|18.5|17.6|16.5|21.0|30.2|20.5|
|*fromremittances|1.2|1.9|1.6|2.8|1.3|2.4|1.6|1.7|2.3|2.6|2.1|
|*other(rents,dividends,<br>etc)|12.1<br>|11.7<br>|10.9<br>|12.4<br>|10.8<br>|10.6<br>|9.6<br>|11.1<br>|13.4<br>|31.0<br>|16.5<br>|
|Total|100|100|100|100|100|100|100|100|100|100|100|

_Source:_ CSES Survey 2001 Table 1 shows that income from self-employment<sup>is the most importantsourceof income</sup> for the vast majority of the population,<sup>only falling substantiallybelow 70% of income</sup> for the top two deciles. Most of<sup>this income comes from cultivation, particularly rice</sup> cultivation, but livestock and forestry<sup>productsarealso of importanceto the poor.The</sup> 7
# **Services in the Trans-Pacific Partnership:  What Would Be Lost?**

Batshur Gootiiz and Aaditya Mattoo _JEL classification codes_ : F13, F14, L80 _Keywords_ : Services trade barriers, TPP, regionalism, STRI, liberalization of trade in services

*World Bank, 1818 H Street NW, Washington DC 20433.  Contact information: Batshur Gootiiz is a consultant to the World Bank <u>(bgootiiz@gmail.com) and Aaditya Mattoo is a research manager at the World Bank</u> (amattoo@worldbank.org).  The authors would like to thank Deanna Easton, Jonathan McHale, Martha Lawless, Christopher Melly, Anne Katrin Pfister, and Sebastian Saez for valuable comments and helpful discussions.  We are grateful for updates on policy information from the following law firms:  Cheok Advocates &Solicitors in Brunei, Guerrero Olivos in Chile, Raja, Daryl and Loh in Malaysia, Hogan Lovells BSTL in Mexico, and LuatViet Advocates & Solicitors in Vietnam. This paper is part of a World Bank research project on trade in services, supported in part by the Multidonor Trust Fund for Trade and Development and by the Strategic Research Program.  The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent, or any of the aforementioned individuals or institutions.
tainted by ideological biases, or are biased towards the views of the business elite, or are biased by the recent economic performance of the countries in question (i.e. socalled 'halo effects'). In each case we proposed specific empirical tests and showed that such biases were for the most part not present, or at most were quantitatively unimportant.<sup>4</sup> Another area where we have refined the WGI over time is in the interpretation of changes over time in the aggregate indicators. In Kaufmann, Kraay and Mastruzzi (2005) we developed a dynamic version of the unobserved components model on which the WGI are based and used it to develop formal statistical tests of the significance of changes in governance based on changes in the aggregate indicators. It turned out that a very simple rule of thumb using data from the static version of the WGI was a good approximation to this more complex and formal method of assessing significant changes: if 90 percent confidence intervals in the two periods being compared do not overlap, the observed change in the aggregate WGI is unlikely to signal a statistically -- or practically -- significant change in governance. We have also continuously monitored changes in world averages of governance on our individual data sources, finding no evidence of significant trends in one direction or the other, as a way of validating our choice of units for the aggregate indicators in which world averages are set to be the same over time.<sup>5</sup> We contrast this careful attention to the significance of changes in governance indicators with the practice followed by the vast majority of governance and investment climate related datasets, that report no margins of error whatsoever and hence can provide no guidance to users as to circumstances under which observed changes in the data are likely to signal meaningful changes in unobserved governance. More generally, recognizing the importance of margins of error and the imprecision of country rankings, we do not follow the popular practice of producing precisely ranked "top ten" or "bottom ten" lists of countries according to their performance on the WGI, recognizing that such seemingly precise 'horse races' are of dubious relevance and reliability.

> 4 See Kaufmann, Kraay and Mastruzzi (2004) for a discussion of ideological biases, Kaufmann, Kraay and Mastruzzi (2005) for a discussion of halo effects, and Kaufmann, Kraay and Mastruzzi (2007b) for a discussion of business elite biases.

> 5 As discussed in Appendix D, we also adjust for (small) compositional effects driven by (small) increases in the number of countries covered by the governance indicators since 1996.

5