rafmacalaba/datause-encoder-data huggingface dataset:
|Nam Theun 2 Social and Environment Project<br>Daryl Fields|
|---|
|Nam Theun 2 Social and Environment Project<br>Daryl Fields|
|Nam Theun 2 Social and Environment Project<br>Daryl Fields|
|Nam Theun 2 Social and Environment Project<br>Daryl Fields|
|Nam Theun 2 Social and Environment Project<br>Daryl Fields|
|Nam Theun 2 Social and Environment Project<br>Daryl Fields|
|Nam Theun 2 Social and Environment Project<br>Daryl Fields|
|Nam Theun 2 Social and Environment Project<br>Daryl Fields|
|Nam Theun 2 Social and Environment Project<br>Daryl Fields|
|Nam Theun 2 Social and Environment Project<br>Daryl Fields|
|ECONOMIC MANAGEMENT TECHNICAL ASSISTANCE<br>Alberto Leyton|
|ECONOMIC MANAGEMENT TECHNICAL ASSISTANCE<br>Alberto Leyton|
|ECONOMIC MANAGEMENT TECHNICAL ASSISTANCE<br>Alberto Leyton|
|ECONOMIC MANAGEMENT TECHNICAL ASSISTANCE<br>Alberto Leyton|
|ECONOMIC MANAGEMENT TECHNICAL ASSISTANCE<br>Alberto Leyton|
|ECONOMIC MANAGEMENT TECHNICAL ASSISTANCE<br>Alberto Leyton|
|Andhra Pradesh Hazard Mitigation and EmergencyCyclone Recover Dara Malia Lengkong|
|Andhra Pradesh Hazard Mitigation and EmergencyCyclone Recover Dara Malia Lengkong|
|Andhra Pradesh Hazard Mitigation and EmergencyCyclone Recover Dara Malia Lengkong|
|Andhra Pradesh Hazard Mitigation and EmergencyCyclone Recover Dara Malia Lengkong|
|Andhra Pradesh Hazard Mitigation and EmergencyCyclone Recover Dara Malia Lengkong|
|Andhra Pradesh Hazard Mitigation and EmergencyCyclone Recover Dara Malia Lengkong<br>|
|Andhra Pradesh Hazard Mitigation and EmergencyCyclone Recover Dara Malia Lengkong|
|Andhra Pradesh Hazard Mitigation and EmergencyCyclone Recover Dara Malia Lengkong|
|Andhra Pradesh Hazard Mitigation and EmergencyCyclone Recover Dara Malia Lengkong|
|Andhra Pradesh Economic RestructuringProject<br>Rajni Bajpai|
|Andhra Pradesh Economic RestructuringProject<br>Rajni Bajpai|
|Andhra Pradesh Economic RestructuringProject<br>Rajni Bajpai|
|Andhra Pradesh Economic RestructuringProject<br>Rajni Bajpai|
|Andhra Pradesh Economic RestructuringProject<br>Rajni Bajpai|
|Andhra Pradesh Economic RestructuringProject<br>Rajni Bajpai|
|Andhra Pradesh Economic RestructuringProject<br>Rajni Bajpai|
|Andhra Pradesh Economic RestructuringProject<br>Rajni Bajpai|
|Andhra Pradesh Economic RestructuringProject<br>Rajni Bajpai|
|Reconstruction and Local Development Project<br>Manuel Salazar|
|Reconstruction and Local Development Project<br>Manuel Salazar|
|Reconstruction and Local Development Project<br>Manuel Salazar|
|Reconstruction and Local Development Project<br>Manuel Salazar|
|Reconstruction and Local Development Project<br>Manuel Salazar|
|Reconstruction and Local Development Project<br>Manuel Salazar|
|Reconstruction and Local Development Project<br>Manuel Salazar|
|Reconstruction and Local Development Project<br>Manuel Salazar|
|Reconstruction and Local Development Project<br>Manuel Salazar|
|Reconstruction and Local Development Project<br>Manuel Salazar|
|ENERGY ACCESS PROJECT<br>Issa Diaw|
|ENERGY ACCESS PROJECT<br>Issa Diaw|
|ENERGY ACCESS PROJECT<br>Issa Diaw|
Gelos, M.G. (2006). “Banking Spreads in Latin America”. IMF Working paper 06/44. International Monetary Fund.

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Jorgensen, O.H. and A. Apostolou (2013). Brazil’s Bank Spread in International Context: From Macro to Micro Drivers, Report No. 6611, World Bank Kaufmann, D., A. Kraay, and M. Mastruzzi (2010). “The Worldwide Governance Indicators: Methodology and Analytical Issues”. World Bank Policy Research Working Paper 5430. World Bank Group.

Kirkegaard, J. and C. Reinhart (2012), “The Return of Financial Repression in the Aftermath of the Great Contraction”, Peterson Institute Working Paper.

Levine, R. (2004). “Finance and Growth: Theory and Evidence”. National Bureau of Economic Research Working Paper No.10766.

Lopes, F.L. (2014). On high interest rates in Brazil. _Revista de Economia Política_ 34(1): 3–14.

McKinnon, R. I. (1973), Money and Capital in Economic Development, Washington, DC: Brookings Institute.

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34
beneficial to concentrate initially on improving transport links with regional partners. In saying this, we are conscious that infrastructure upgrading can involve significant costs. Whereas we emphasize the relative benefits of different options in this paper, informed policymaking would of course need to be based on a rigorous cost-benefit assessment in each case.

Second, e-business and connectivity would also seem to be very important for the region (see _Scenario 4_ ). ASEAN member countries may therefore also benefit from giving priority to improvement of information technology infrastructure and, more generally, the competitiveness of backbone services sectors. Such an agenda could include both domestic regulatory reforms, and increased openness to international services trade in these sectors under the GATS.

Finally, there is still progress to be made in reducing “traditional” trade barriers (such as tariffs), and in improving governance and transparency in such a way as to limit the role of unofficial payments in import/export transactions. While significant economic benefits could flow from these steps, the data indicate at this stage that it may be preferable from a trade flow point of view to concentrate most heavily on the other two areas in the short-term. These conclusions are broadly consistent with the data presented earlier indicating that tariff protection in Southeast Asia is generally at a low to moderate level. However, those same data—based on the Kee et al. (2006) OTRI—suggest that non-tariff barriers may play a significantly more important role. Due to lack of information at this point, we have not explicitly considered the role of non-tariff barriers in our gravity model. This would be an important point for future research to elaborate on, so as to give reform of these measures an appropriate place in the policy ranking we are suggesting.

Before concluding this Section, it is important to stress that our results, like all simulation results, are subject to a number of caveats. First, our results in relation to tariffs, maritime port

- 22 -
_Figure 8: The impact of demonetization was short-lived at the aggregate level._ <!-- Start of picture text --> 15<br>10<br>5<br>0<br>Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar<br>2016/17 2015/2016<br>Nightlight intensity<br><!-- End of picture text --> <!-- Start of picture text --> Nightlight intensity<br><!-- End of picture text --> On the other hand, the local impact may have been large in more informal districts, where cash must have played a more important role in supporting transactions. Identifying informal areas is not straightforward, but we assume that informality is higher in rural districts, in districts with low access to finance and in those where regular wage workers account for a lower share of total employment. The spatial approach is used to estimate quarterly GDP at the district level, based on local nightlight intensity and rural population. Local GDP levels are then used to compute local growth rates, and to assess how they were affected by demonetization.

In India’s case there is evidence that poorer states have grown more slowly, and these poorer states may also be characterized by higher levels of informality. If so, just comparing growth rates across formal and informal districts would overestimate the impact of demonetization. To address this possible bias, much the same as in Nepal’s case, a “difference-in-differences” approach is used. The first difference is between the district-level GDP growth rate in the third quarter of 2016/17 and that in the previous year.<sup>7</sup> As before, this first difference

> 7 Considering the average growth rate of the three previous years instead did not change the results.

21
Table 6: Consistency in Responses on Sensitive Information with Additional Surveys

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Cons|istency w|ith Baselin|e Survey|Cons|istency w|ith Endlin|e Survey|
||Stress|high|Physical|punishment|Stress|high|Physical|punishment|
||||is eff|ective|||is ef|fective|
|Phone survey|0.032|-0.053|-0.066|-0.103|0.067|-0.083|-0.076|-0.022|
||(0.079)|(0.137)|(0.074)|(0.129)|(0.090)|(0.154)|(0.089)|(0.157)|
|Mean of Dep. Var (WhatsApp)|0.574|0.574|0.735|0.735|0.566|0.566|0.642|0.642|
|R-squared|0.129|0.132|0.159|0.166|0.163|0.177|0.153|0.177|
|Observations|207|207|207|207|169|169|169|169|
|Controls (Age, Sex)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Interviewer FE|No|Yes|No|Yes|No|Yes|No|Yes|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ This table shows the estimated impacts of the phone-based survey relative to WhatsApp-based survey on consistency in responses on sensitive information. “Phone survey” is a dummy variable equal to one if the survey was per call and zero if was assigned to the WhatsApp group. The dependent variables is a dummy equal to one if the participant was consistent on her self-report of stress and her perception about physical punishment effectiveness between pairs of surveys (See more details in Table A8). The control variables include age in years and sex of the respondent. To account for potential time-invariant characteristics within municipalities, we include municipality fixed effects. As robustness check, we include education level as control variable. Results are presented in Table A5 in the Appendix. Robust standard errors are in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

34
evidence of stronger credit constraints for smaller (and younger) firms. Our results are also generally consistent with those from the _Reserve Bank of India_ (2005).<sup>22</sup> The patterns above appear to suggest that external long-term finance has remained an important bottleneck in Indian industry, particularly for the SME sector. Recent studies by other researchers have found evidence of “under-lending” by Indian banks to the corporate sector. Underlending is present when the marginal rupee lent to a borrower yields a higher marginal product than its interest cost.<sup>23</sup> Banerjee and Duflo (2003) find that, even after six years of liberalization, bank credit was scarce while interest rates, though high by world standards, appeared to be under the equilibrium levels. It is a system-wide feature, indicating that companies cannot get adequate credit, not just from a single bank but from the banking system in general. In many countries, the SME sector faces impediments to bank financing, and depends heavily on short-term borrowing.<sup>24</sup> India perhaps presents a rather extreme example of this phenomenon, with the SME sector facing a severe shortage of financing. About 50% of the respondents in a recent National Sample Survey Organization (NSSO) survey said that they faced an acute shortage of capital.<sup>25</sup> The mean loan outstanding was less than 3% of gross fixed asset (at market prices). About 93% of SME units had no outstanding loans from banks or financial institutions. About half of the respondents in the NSSO survey without loans mentioned that getting a loan is very difficult. About half of the loans to this sector came from specialized central financial institutions like the Small Industry Development

> 22 Using financial reports of around 2,000 _public_ companies, the _Reserve Bank of India_ finds that internal sources accounted for about 40% of total funds until 1999-2000, and then jumped to about 60%. They treat “provisions” as part of “internal sources”, while our data source, CMIE Prowess, includes it in “others” category.

> 23 For example, Banerjee et al. (2004) estimate that, for profitable firms (mean profit Rs. 36,700) in India, an increase of Rs. 1,000 in lending (average loan size Rs. 86,800; not fixing other financing sources) causes an increase in annual profit of Rs. 756.13. This finding indicates that companies may enhance profits by borrowing more from the banks.

> 24 See Voordeckers and Steijvers (2005) for the Belgian case, and see Poutziouris et al. (2005) for the situation in the U.K.  In the U.S., small firms also have difficulties in obtaining bank loans, but part of the funding slack has been provided by private equity (including angel financing and venture capital) and privately placed and public bonds (Berger and Udell 1995, 1998).

> 25 “The Indian SME Sector” – A report by India Development Foundation and Indicus Analytics (March 2004).

20
Table 9 Brady-Plan Countries: Debt Reduction in DDSR Operations as a Percent of the Initial Shortfall

||Debt|Price|Shortfall|Net Debt<br>Reduction|Debt Reduction<br>as Percent of Shortfall|
|---|---|---|---|---|---|
|Costa Rica|4.8|0.56|<br>2.1|1|47.3|
|Mexico|100.4|<br>0.36|<br>64.3|16.6|25.8|
|Philippines|29.4|0.46|<br>15.9|0.5|3.1|
|Uruguay|3.6|0.36|<br>2.3|0.7|30.4|
|Venezuela|34.8|0.3|<br>24.4|4.3|17.7|

Source: Data from Fernandez-Arias (1992)
15 in countries such as Poland, Turkey, Czech Republic, Bulgaria, Slovakia, and Croatia. Bank privatizations also took place in Latin America (Mexico and Brazil), East Asia (China, Philippines, Indonesia and Thailand), South Asia (Pakistan), and sub-Saharan Africa (Nigeria, Uganda).

# _Competitive sector_

24. Proceeds in competitive sectors, comprising largely manufacturing and service firms, had the largest number of transactions, registering at over 6,000. Manufacturing firms accounted for over two-thirds of competitive sector proceeds for the period as a whole, while service and other sectors accounted for the rest. The largest transactions in manufacturing were in steel, cement, fertilizer and other chemical sub-sectors. Countries in Europe and Central Asia were the most active in manufacturing, while Latin American countries divested the largest share in service and other sub-sectors. Proceeds in the competitive sectors declined from a 22 percent share of total proceeds in 1990-1999 to 9 percent in 2000-2003, reflecting in large part the shift to activity in infrastructure and finance.

# _Other sectors_

25. Energy sector proceeds (oil and gas, petrochemicals) remained more or less steady: transactions over the period as a whole were concentrated in a few countries—Argentina, Brazil, China, South Africa, Kazakhstan, Russia and Poland. The primary sector declined slightly, with most of the revenues concentrated in Latin America, Europe and Central Asia, and Sub-Saharan Africa.

# **III.    GOVERNMENT OWNERSHIP:  WHERE IS IT AT?**

26. The privatization of more, and more important, enterprises would tend to suggest that government ownership has declined over the years. But a systematic assessment is difficult in the absence of data on state enterprise sectors, in particular their share of GDP, investment, domestic credit, formal sector employment, and the like. Such data was last captured for industrial and developing countries for the period 1978-91 in the World Bank’s Bureaucrats in Business Report (1995). Since then the 2000 World Development Indicators updated a few measures for a few selected countries, covering the period 1990-97. But spotty and relatively old data continue to make a systematic analysis difficult.

27. Nevertheless, anecdotal information shows that while privatization activity has grown, it appears at the aggregate level to be still small relative to the stock of state enterprises. Since 1990, the average number of transactions per country per year has been only five, compared to three in the earlier time period from 1980 to 1993 (World Bank 1995), and compared to a much larger number of enterprises that could have been divested in almost all countries. Moreover, most countries in fact fell short of the average given the concentration of transactions in a few countries. This suggests that after more than 20 years of privatization, government ownership is still prevalent in many developing regions and in certain sectors more or less across all regions of the world.
# **Figure 2: Actual and Predicted Annual 2008-2012 CERs**

<!-- Start of picture text --> 700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>-100<br>Start of CDM Public Comment Period<br>All Projects CERs 2012 Model 1 Model 2<br>Cumulative CERs, MtCO2e<br><!-- End of picture text --> 21
North, D. C. 1971. _Structure and Change in Economic History_ . New York: W.W. Norton.

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Rozelle, S. and J. F. M. Swinnen. 2004. "Success and Failure of Reform: Insights from the Transition of Agriculture." _Journal of Economic Literature 42_ (2): 404-56.

Saxena, N. C. 2005. "Updating Land Records: Is Computerisation Sufficient?" _Economic and Political Weekly 40_ (4): 131-46.

Stiglitz, J. E. and A. Weiss. 1981. "Credit Rationing in Markets with Imperfect Information." _American Economic Review 71_ (3): 393-410.

Transparency International India. 2005. "India corruption study 2005." . New Delhi: Transparency International.

Wadhwa, D. C. 2002. "Guaranteeing Title to Land The Only Sensible Solution ." _Economics and Political Weekly_ .

World Bank. 2007. "India: Land policies for growth and poverty reduction." . New Delhi: World Bank Agriculture and Rural Development Sector Unit South Asia Region and Oxford University Press.

World Bank. 2009. _Doing Business in 2010: Reforming through difficult times_ . Washington, D.C.: World Bank, International Finance Corporation, and Oxford University Press.

24
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# 1. Introduction

The standard approach to assessing education's effect on labor market outcomes, particularly income, is to estimate some variant of the human capital earnings function, in which earnings are specified as a function of years of schooling and work experience [Mincer (1974)). This approach presents relatively few problems when the analysis is confined to employees, for whom income is largely in the form of wages and for whom, therefore, the regression coefficient on years of school can be interpreted as the private return to investment in schooling. The model performs best in the case of wage employees who work continuously after completing their schooling. For self--employed workers, however, application of the usual human capital earnings function raises methodological issues that most empirical studies have failed to address satisfactorily.

First, with the exception of a growing number of studies of smallscale farming (for a survey of this research, see Lockheed, Jamison, and Lau 1980] and only a very few studies of nonfarm enterprises in developing countries (e.g., Strassmann (1987); Blau (1985); Teilhet-Waldorf and Waldorf (1983)], most of the research on the self-employed has taken the individual as the unit of analysis rather than the enterprise, thereby ignoring the contributions to income of capital and other nonlabor inputs. When two or more people work in the same enterprise, and none of them is an employee of another, there is a further problem of how income is shared among the workers in the business, but the problem of nonlabor factors in generating the income remains even when the enterprise consists of a single worker. The result is not just an asymmetry between tl'e treatment of farm and nonfarm family businesses, but far more serious, the likelihood of upwardly biased estimates 1