rafmacalaba/datause-encoder-data huggingface dataset:
(such as teamwork) – which may also be learned in schools? Finally, should efforts be directed in developing assessments of non-literacy labor market relevant skills?

# **5. Conclusion**

Past evidence suggests that a very large part of the returns to schooling are generated by effects or correlates unrelated to literacy skills measured by the available tests. Using an approach which splits the measure of cognitive skills available between those acquired through schooling vs. those acquired elsewhere, and using data from the International Adult Literacy Surveys (IALS) and the Adult Literacy and Life Skills surveys (ALL), we obtain multi-country estimates of the components of the return to schooling.

It is found that there is large heterogeneity between countries. For a subgroup of countries with effective education systems, which produce workers well-endowed in labor market relevant functional skills, the labor market rewards these skills substantially, along with other, more difficult to measure skills. On the other hand, for countries with less effective education systems, only a very small part of the reward can be attributed to literacy skills; the coefficient of years of schooling largely reflects the signaling value of schooling. These results seem to suggest that, at least for the latter group of countries, a substantial component of the returns to schooling is generated by effects which are unrelated to skills measured by available tests. Correspondingly, the contribution of more literacy skills to earnings is substantially independent of schooling.

19
<!-- Start of picture text -->
-<br>Figure 4a: Cross Sectoral Flows (F S) (Trends and Cycle): Mexico an Brazil<br>Mexico   Brazil<br>1987q1 1991q3 1996q1 2000q3 2005q1 1983q1 1988q1 1993q1 1998q1 2003q1<br>S-F F-S S-F F-S<br>1987q1 1991q3 1996q1 2000q3 2005q1 1983q1 1988q1 1993q1 1998q1 2003q1<br>S-F (HP) F-S (HP) S-F (HP) F-S (HP)<br>.12<br>.04<br>.1<br>.03<br>.08<br>.06<br>.02<br>.04<br>.01<br>.02<br>.2<br>.1<br>.1 .05<br>0<br>0<br>-.05<br>-.1<br>-.1<br>-.15<br>-.2<br><!-- End of picture text -->

Notes: The graph shows the transition rates between informal self-employed (S=Informal Self-Employed) and the formal sector (F=Formal Sector). Transition rates are inferred from the continuous time transition matrix for each period obtained following the procedure by Geweke et al. (1986) outlined in Section III. Computations are based on 10.000 Monte Carlo replications. The series have been smoothed using a 4 quarter moving average to remove high frequency fluctuations. The bottom panels shows the series logged and de-trended series using an HP filter with lambda 1600. Data for Mexico (left panels) is drawn from the quarterly National Urban Labor Survey (ENEU) from 1987:Q1 to 2004:Q4. Data for Brazil (right panels) is drawn from the Monthly Employment Survey (PME), quarterly averaged from 1983:Q1 to 2001:Q2. Shaded areas indicate recessions.

35
# **RETURN TO INFORMAL GOVERNANCE OF TRADE CONTROLS**

Table 3.2 reports the Global Trade Alert (GTA) tabulation of trade restrictions and tradeaffecting industry support measures. The GTA, a project supported by the World Bank and other organizations, is motivated by the concern that the Great Recession might bring a resurgence of trade restrictions. In addition to including all measures for which the WTO Agreements requires notification, GTA covers other measures (some tailored perhaps to elude WTO restrictions) reported by news media, as well as by importers and exporters.<sup>13</sup> The table documents not only that Argentina has applied a number of trade control measures, it also documents that the procedural reforms of the 1990s, to a large extent, have been put aside. Sixty percent of the import controls by Argentina have been measures that the Global Trade Alert classifies as “non-tariff barriers (not otherwise specified)” and other measures not put in place through formal procedures. Moreover, procedures for antidumping and safeguards – classified in the table as “formal” – have been made less inclusive of stakeholders, for example public hearings have been eliminated from the proceedings through which the Foreign Trade Commission (CNCE, Comisión Nacional de Comercio Exterior) conducts these investigations.<sup>14</sup> The import restrictions applied have taken many forms: unexplained delays in release from customs, additional documentation/process requirements, non-responses and delayed responses to requests for import licenses, lack of transparency in the process by which decisions are taken, control of technical regulations, volatility in application of standards. For example, in September 2011 several Argentine publishers were not able to obtain the release from customs of almost 2 million books. Many of these books had been written and edited in Argentina, but shipped outside for printing. In separate meetings with government officials, each publishing company was told to provide a plan on how it would in the future have its printing done in Argentina, and how it would change its product lines in instances in which specialized printing was not available domestically. Table 3.1 provides other examples.

A common element in the actions reported is that the eventual release of imports depended on reaching agreement with the government on commitments to countertrade, to substitute in the future domestic products for previously imported ones and sometimes to make investment in domestic facilities.

> 13 Here, the contribution of investigative journalism to alert citizens to policies in place, their costs and who is profiting from them has been an important part of trade liberalization.  We have found however only one analysis of their role, McCarthy 2000, who examines their input in Australia.

> 14 “Tariff changes,” likewise classified for the table as “formal” procedures, may not be made through formal processes.  Dantas (2012) explained that neither Argentina or Brazil have established participatory procedures for the tariff changes they have made under Mercosur’s allowed flexibilities.

17
||**Table 1. L**|**abor force partici**|**pation rates**||
|---|---|---|---|---|
||Abidian|Other Cities<br>|I Rural Areas|C8te d'Ivoire|
|||1985|||
|Very poor|29.5|47.1|67.9|63.7|
|Mid-poor|28.1|40.5|64.7|59.6|
|Non-poor|33.8|40.0|64.9|50.3|
|AI|1<br>33.6|..<br>40.6<br>1988|65.21_|<br>53.4|
|Very poor|_|43.8|69.0|66.4|
|Mid-poor|21.9|43.3|66.9|58.0|
|Non-poor|33.5|37.9|64.2|49.7|
|All|32.0|40.0|66.1|54.4|

Table 1 shows the labor force participation rates in 1985 and 1988, by poverty status. Labor force participation is defined here as any form of economic activity for wages or own account, whether paid or not. Labor force participants thus consist of employees, employers, self-employed, and unpaid family workers. Two conclusions stand out from Table 1. First and foremost, labor force participation is _inversely_ related to welfare level: the very poor have the highest rate of participation. This immediately dispenses with the hypothesis that the poor are poor because they do not participate in the labor force. Second, participation is much higher in rural than urban areas. This is consistent with the previous finding, since poverty in Cote d'Ivoire is higher in rural areas. (The sole exception seems to be Abidjan, where the participation of the non-poor is higher.) Between 1985 and 1988, labor force participation did not change much. Only the very poor showed a slight increase, which suggests that the increase in 8
19 free’ state. Non-independent states (such as the Falkland Islands) are also coded as ‘partly free’. Dismissing such cases and using the Polity IIId data with more precise dating (Mitchell et al., 1998), Tures finds only 13 cases of democracies intervening in other democracies over the longer time-span 1945–91. Four of these are interventions by Rhodesia and South Africa in Botswana. Because it relies only on institutional characteristics (and pays no attention to the level of participation) the Polity project codes South Africa under apartheid (and Rhodesia after its unilateral declaration of independence, when blacks were still denied the vote) as democracies. This startling classification makes little difference to most statistical studies, but exercises a strong influence on these results. The remaining five interventions are all in the two dyads India–Pakistan and Peru–Ecuador. Even including the interventions by Rhodesia and South Africa, Tures concludes – contrary to Kegley & Herman – that dyadic democratic interventions occur much less frequently than one would expect on the basis of the distribution of regime characteristics and the number of democratic interventions. The extensive targeting by democracies of other democracies appears to be a red herring. Pickering (1999) found that democracies with adequate military strength were less frequent targets of military intervention generally in the period 1975–1996.

But the fact remains that democracies frequently intervene militarily and Hermann & Kegley also investigated the motives and effects of these interventions. Kegley & Hermann (1997a) showed that two-thirds of the democracies’ interventions were undertaken to expand or defend the liberal democratic community. Moreover, Hermann & Kegley (1998) showed that military interventions undertaken in order to protect or promote democracy generally led to an increase in the level of democracy in the target states. In a study of 106 developing countries from 1960 to 2002, Pickering & Kisangani (2006) found that hostile interventions can help to democratize non-democratic targets. Pickering & Peceny (2006) found a strong statistical relationship between US hostile military intervention and democratization, but this result seems to be driven by three cases in the Caribbean. Another note of skepticism is sounded by Bueno de Mesquita & Downs (2006), who argue that the citizens in a transformed democratic target are likely to have different policy priorities than those of the winning coalition in the intervening country. Empirically, they find for the period 1946–2001 that intervention does little to promote democracy when comparing it with a counterfactual trajectory without intervention.
The monetary value of this electricity trade, especially for Nepal and Bhutan would be huge as compared to their economic output or GDP. At present Nepal is importing electricity from different parts of India with price ranging from Indian Rs. 3.98 to 6.45 per unit or kWh<sup>17</sup> (US$ 0.06 to 0.10), with a simple average price of US$0.08/kWh. If Nepal exports 3,608 TWh (or billion kWh) under the regional electricity trade scenario during the 2020-2040 period, it generates US$289 billion over that 20-year period at the 2018 price. This amounts to 10 times as much as Nepal’s GDP in 2017. This estimate simply indicates how big would be the size of electricity export revenues for Nepal if regional electricity trade creates markets for Nepal’s hydropower resources. Similar benefits would occur to Bhutan and Afghanistan although the magnitudes of their electricity export revenues would be smaller as compared to Nepal’s. India will benefit in the similar way as its power supply costs reduce due to import of cheaper hydropower from neighboring countries.

# **5.  Conclusions**

South Asia is endowed with a huge potential of hydropower generation (> 350 GW) of which more than 80% is yet to be exploited. One of the primary reasons behind the limited exploitation of hydropower potential in the region is limited provisions to trade electricity across the borders. Among several possible factors, this study identifies six that are critical to promote hydropower development in South Asia through the expansion of regional electricity cooperation and trade. First, Afghanistan, Bhutan and Nepal would have surplus hydropower potentials even under highly optimistic scenarios on the growth of their domestic demand. Access to markets (i.e.,

> 17 The Himalayan Times, March 29, 2018. https://thehimalayantimes.com/business/nepal‐electricity‐authority‐ renews‐power‐purchase‐agreement‐with‐ntpc‐vidyut‐vyapar‐nigam‐for‐15‐months/

32
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In stark contrast to the laboratory results and the networks survey, traditional survey measures of self-reported behavior and beliefs about the community did indicate a significant impact of the program on both the retrospectively self-reported social action and the respondents’ characterizations of the pro-sociality of their communities. There was no evidence from the behavioral laboratory measures that villagers in treated communities were more pro-social, but villagers in those communities believe that they are. While we have no smoking-gun evidence that the observed behaviors in the lab are the better measures and the self-reported retrospective behaviors are biased, that was the hypothesis with which we began this study based on concerns in the literature about potential bias in retrospective surveys of self-reported behavior. That hypothesis is certainly supported by our results. If, notwithstanding, one chooses to believe the self-reported survey results, then our laboratory results and networks survey clearly indicate that the greater self-reported civic participation in program communities was not due to an increase in pro-social preferences or to denser social networks and must have been due to other changes in the communities such as local governing institutions or the mobilization efforts of the program itself.

Our laboratory measures of pro-social behavior and the networks survey have allowed us to pinpoint one (although certainly not the only) answer to the question of _how_ the CDF program failed to create social capital. The question of _why_ the program failed would be speculative, but we have already alluded to one possible reason: There is really nothing in the Putnam model of social capital formation that suggests that a program like this _should_ create social capital. The program was very assiduous in dispatching social mobilizers to program villages to lecture them on the importance of social capital, collective action, inclusiveness, citizen participation, social responsibility, trust and trustworthiness. But in the Putnam model social capital is not created by people being told it is important; it is created organically when people associate with each other in enjoyable social interactions. In that model, social capital is not created in civics class but in the pleasant day-to-day activities that people undertake collectively. CDF and, indeed no CDD program of which we 35
of the state’s population, and the log of per capita income in the state. These are discussed in more detail in the next section.

Hirschman (1958) suggests that entrepreneurial talent might limit entry of firms in developing countries. Entrepreneurial talent is also, of course, central to the model developed by Lucas (1978). We have no direct measures of how the stock of entrepreneurial talent varies across states in Mexico, but we measure this with educational attainment rates. One issue in the data is that educational attainment and per capita income are highly correlated, making it difficult to separate the effects of the two variables.

Finally, there is also a literature on the strategic behavior of large firms with respect to preventing entry (Gilbert, 1989). Dominant firms may also capture government officials, encouraging them to harass those firms who do enter and preventing them from growing. Given the very large number of very small firms, we do not expect the entry story is relevant in Mexico. But we will test for whether large firms have the effect of preventing growth among smaller firms. We also test to see if the presence of foreign firms operating in a state and sector affects the size of firms.

# **Section 3: The Data**

Our data on firm investment and employment levels come from the Mexican Economic Census of 1998 (INEGI 1998). Employment and investment data are given in reference to December 31, 1998. The economic census covers the manufacturing, commerce, services and construction sectors. Data are gathered for every location of each firm in Mexico, but the statistics institute (INEGI) does not make the firm level data 17
<!-- Start of picture text --> v -<br><!-- End of picture text -->

# Table SC.1 Assignment of Functions to Entities under Reshaping Options (Note: In any one country, options can be combined or altered to fit conditions)

||||Options||__<br>_|
|---|---|---|---|---|---|
|Function|(1) Monolith<br>R=Railway|(2) Lines-of-<br>Business<br>R=Parent<br>L=Line of<br>Business<br>LP=Primary<br>Line-of-Business|(3) Competitive<br>Access<br>-owing<br>Railway<br>R2=Railway<br>havbig<br>Trackage Rights|(4) Wholesaler<br>R=Railway<br>W=Wholesaler|(5) "Toil Rail'<br>Enterprise<br>F=Fixed<br>Facility<br>Owner<br>O=Operator|
|PFxed Facility<br>Ownership|R|R (parent)|RI|R|P|
|Fixed Facility<br>Maintenance|R|LP|RI|R|P|
|Operations Control|R|LP|RI|R|P or 0|
|Train Movement|R|L|RI, R2|R|0|
|Equipment<br>Provision|R|L|RI, R2|R (W)<sup>2</sup>|0|
|Equipment<br>Maintenance|R|LI/|RI,R2,|R (W)<sup>2</sup>/|0|
|Marketing|R|L|R1, R2|W|0|
|Accountability|R|L|RI, R2|R, W|F,0|

- W/ Where one line of business performs equipment maintenance for another, intemal charges would be reciprocally assessed.

- 2v In this case the railway would nornally be the primary supplier, but the wholesaler could well be an optional source of supply for railway rolling stock.

# Table SC.2 Examples of Reshaping Options

|Options|Examples"|
|---|---|
|(1) Monolithic Railway|Bolivia, China, USSR, Zimbabwe|
|(2) Lines-of Business Organization|British Rail, RENFE<sup>21</sup>, United States (as a whole: freight railways,<br>Amtrak,<br>commuter authorities)|
|(3) Competitive Access|Canada (freight; CP and CN are to operate over some parts of each<br>other's track); United States (trackage rights and joint teminal<br>agreements); Council of the European Communities' directive<br>(competitive access concepts)|
|(4) "Wholesaler"|American President Lines (double stack), Norfolk Southern Triple<br>Crown, haulage agreements, Australia|
|_(5)_'Toll Rail" Enterprise|Amtrak, Japan Preight Railway Company, Japan's Shinkansen services,<br>Swedish Railways (Sl)/Banverket, State Railways of Thailand|

- 1' Not all these examples are discussed in the text of this paper.

- 7' Spanish National Railways. A list of acronyms appears at the end of this paper.
Figure X: Mechanisms of Withholding Rate Impact

# Default Mechanism: Incomplete Reclaim

<!-- Start of picture text --> A: Share of Reclaimers B: Share of Withheld Tax Reclaimed<br>Reclaimers over Withholdees Share of Withheld Tax Reclaimed<br>2010m1 2011m1 2012m1 2013m1 2010m1 2011m1 2012m1 2013m1<br>Date Date<br>.7 1.2<br>.65<br>1.1<br>.6<br>1<br>.55<br>Share Share<br>.9<br>.5<br>.45 .8<br>.4<br><!-- End of picture text -->

- Salience of Enforcement Mechanism: Increased Reporting

<!-- Start of picture text --> C: Sales Tax Collected D: Input Tax Credit Deducted<br>DD=-.01 (.008)<br>DD=-.03 (.01)<br>2010m1 2011m1 2012m1 2013m1 2010m1 2011m1 2012m1 2013m1<br>Month Date<br>Treatment Group (Rate Increase) Control Group Treatment Group (Rate Increase) Control Group<br>E: Gross Tax Liability F: Number of Audits<br>DD=.167 (.021)<br>2010m1 2011m1 2012m1 2013m1 2009 2010 2011 2012 2013 2014<br>Month Year<br>Treatment Group (Rate Increase) Control Group N planned audits N planned (non-LTU firms)<br>1.3 1.3<br>1.2 1.2<br>1.1 1.1<br>1 1<br>Change Change<br>.9 .9<br>.8 .8<br>.7 .7<br>1000<br>1.8<br>900<br>1.6<br>800<br>Change 1.4<br>1.2 Number of Audits 700<br>1 600<br>.8 500<br><!-- End of picture text --> Notes: The gure displays evidence on the mechanisms for the withholding rate impact. In all panels, the black solid line marks 08/2011, when the increase in withholding rates entered into eect. Panels A and B display, for all rms subject to withholding in a given month, the share of rms making a reclaim, and the average share of withheld tax reclaimed. The shares are winsorized by the 99.9th percentile. Panels C, D and E show results of the dierence-in-dierence estimation of Equation 2, with dierent outcome variables, as indicated by the gure titles. The treatment group includes rms experiencing an increase in the predicted withholding rate in 08/2011, the control group includes rms experiencing no increase in the predicted withholding rate and rms not subject to withholding. The data is winsorized by the 99th percentile, and scaled by the pre-09/2011 average. The text displays the coecient _β_ (marginal eect compared to prereform average) from estimating Equation 2. Panel F shows the evolution over time of the number of planned audits for all taxpayers and for taxpayers who are part of the large taxpayer unit, as per the annual work programs of the audit department. 46