rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 3: Distribution of Estimates of the Impact of Cash Transfers on Temptation Goods by Program Type**

**Panel A: Conditional cash transfers – All estimates**

**Panel B: Unconditional cash transfers – All estimates**

<!-- Start of picture text --> Negative & Negative &<br>significant significant<br>Negative or zero Negative or zero<br>estimatesCCTs: All 9 17 4 1 & insignificant* estimatesUCTs: All 2 6 2 1 & insignificant*<br>Positive & Positive &<br>insignificant insignificant<br>Positive & Positive &<br>significant significant<br>0% 50% 100%<br>0% 50% 100%<br><!-- End of picture text -->

**Figure 4: Distribution of Estimates of the Impact of Cash Transfers on Temptation Goods by Region**

**Panel A: Latin America - All estimates**

**Panel B: Other regions – All estimates**

<!-- Start of picture text --> Negative &<br>significant Negative &<br>significant<br>Negative or zero<br>Latin America:All estimates 9 13 5 1 & insignificant* Other regions: 3 11 1 1 Negative or zero& insignificant*<br>Positive & All estimates<br>insignificant Positive &<br>insignificant<br>Positive &<br>significant Positive &<br>significant<br>0% 50% 100%<br>0% 50% 100%<br><!-- End of picture text --> 30
**<u>Table 19: Categorization of minimum wage levels internationally</u>**

|**Minimum wage level**|**MW as percentage of average wage**|
|---|---|
|Low|Less than 20%|
|Modest|20-29%|
|Medium High|30-39%|
|High|40-49%|
|VeryHigh|50% or more|

Source: Rutkowski, 2003.

- Further deepening of credit initiatives would be beneficial in the face of widespread demand and relatively limited start-up financing needs. While progress has been made, the “missing middle” of credit markets in Timor Leste will continue to constrain the ability of firms to grow and generate the additional employment that is needed in urban areas of the country.  The capacity for deepening credit markets will depend in part on a strengthened environment for contract enforcement, which is a key element of building the confidence of credit providers to expand coverage.

- Skills enhancement is widely acknowledged as a need at the micro and macro-levels. Higher skills in the workforce are critical to achieving higher productivity, which would in turn stimulate growth and employment opportunities.  However, the lack of confidence in the current public training system suggests that a successful strategy would comprise several elements, with a strong role for the private sector.  Suggestions which the UES and other work suggest include:

- Public sector training could be strengthened through a greater emphasis on short courses, which are the ones most demanded by employers.  Current long courses appear not to be achieving skills improvement to an extent that would justify their high unit costs.

- A common accreditation and certification system for training providers would be beneficial.  There has been significant preparatory work in this regard, and it is a clear demand from employers.

- Involvement of the private sector in both the development of the accreditation system and in development of training curricula would pay dividends in terms of improving skills matching in the country.

- Consideration could be given to piloting demand-side interventions to those seeking to upgrade their skills.

34
> Table **5** presents a break-up of these changes in returns. The results show that for salaried workers in nonfarm activities, most of the increase in returns has to do with a large increase in the constant, which measures average real non-farm wages holding everything else constant. This increase could be due to either higher productivity, or higher relative prices, or a combination of both. The finding of sustained increase in real wages over the decade is in line with results from alternate surveys conducted by the Central Bank, which show a sharp increase in services and government real wages in from 2002 to 2013, and in industry real wages in 2013 (Figure 11 **Error! Reference source not found.** ). In addition to an average increase in real wages, returns to salaried non-farm workers increased due to an increase of returns to experience, which accounts for more than 2 percentage point reduction in poverty. Finally, note that returns to education actually declined for salaried non-farm workers, leading to an increase in poverty when everything else is held constant. In other words, higher levels of education obtained a lower wage premium in 2012/13 when compared to 2002.

The constant term is also the most important factor in explaining the change in returns for self-employed and salaried farm workers (see 10
# **References**

. Anderson, R., and R. May. 1992. _Infectious Diseases of Humans: Dynamics and Control_ Oxford, UK: Oxford University Press.

Angrist, J. 1996. Conditioning on the Probability of Selection to Control Selection Bias. NBER Technical Paper: No. 181.

Ansari, S., S. Sattar, V. Springthorpe, G. Wells, and W. Tostowaryk. 1989). In vivo protocol for testing efficacy of hand-washing agents against viruses and bacteria: Experiments with rotavirus and Escherichia coli. _Applied and Environmental Microbiology_ 55, 3113–3118.

Babor, T., R. Caetano, S. Casswell et al. 2003. Alcohol: No ordinary commodity: Research and public health. Geneva: Oxford University Press/World Health Organization.

Banerjee, A., S. Cole, E. Duflo, and L. Linden. 2007. Remedying education: Evidence from two randomized experiments in India. _The Quarterly Journal of Economics_ , Vol. 122 (3), pp. 1235– 1264.

Banerjee, A., and E. Duflo. 2011. _Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty_ . Public Affairs, New York.

Banerjee, A., R. Glennerster, and D. Kothari. 2010. Improving immunization coverage in rural India: A clustered randomized controlled evaluation of immunization campaigns with and without incentives. _British Medical Journal_ , 2010; 340: c2220.

Black, R., S. Morris, and J. Bryce. 2003. Where and why are 10 million children dying every year? _Lancet_ 361, 2226–2234.

Bowen, A., H. Ma, J. Ou, W. Billhimer, T. Long, and E. Mintz. 2007. A cluster-randomized controlled trial evaluating the effect of a handwashing-promotion program in Chinese primary schools. _American Journal of Tropical Medicine and Hygiene_ 76(6): 1166–1173.

Campbell, K., E. Water, S. O’Meara, and C. Summerbell. 2001. Interventions for preventing obesity in childhood: A systematic review. _Obesity Reviews_ 2, 149–157 Cattaneo, M., S. Galiani, P. Gertler, S. Martinez, and R. Titiunik. 2009. Housing, health, and happiness. _American Economic Journal: Economic Policy, American Economic Association_ , 1(1), pages 75–105, February.

Chapman, S. 2010. Evaluating social marketing interventions. _Evaluating Health Promotion: Practice and Methods_ , 3rd ed. Eds. Margaret Horogood and Yolande Coombes, 105–120. Oxford: Oxford University Press.

Clasen, T., D. Fabini, S. Boisson, J. Taneja, J. Song, E. Aichinger, A. Bui, S. Dadashi, W. Schmidt, Z. Burt, and K. Nelson. 2012. Making sanitation count: Developing and testing a device for assessing latrine use in low-income settings. _Environmental Science & Technology_ , forthcoming.

33
Excluding the loss of tariff revenue from the model nearly doubles the estimated average income gains from trade to 3.7% on average across countries (relative to 1.9% in the main model). Moreover, income gains are now positive in 53 out of the 54 countries. Yet, equality gains continue to be negatively correlated with income gains and trade-offs remain widespread.

If instead of assuming away tariff redistribution we assume that the government makes up for the loss in tariff revenue by imposing progressive taxes (calibrated using the World Tax Indicators (WTI) database (Andrew Young School of Policy Studies, 2010)), the inequality costs of trade liberalization show some minor difference, but the overall pattern of results is not impacted. Income gains are negatively correlated with equality gains. Yet, the latter typically are dominated by the former such that countries facing trade-offs are typically better of with freer trade.<sup>15</sup>

# **5.7 Protectionist Scenarios**

Thus far, we have analyzed the inequality-adjusted gains from liberalization, but our framework also lends itself to evaluating trade-offs that might arise if countries become more protectionist. To explore this, we evaluate the welfare impacts associated with three protectionist scenarios that move the economy closer to autarky: (i) a uniform increase in tariffs of 10 percentage points (i.e. adding 10% to all existing tariffs); (ii) a relative increase in tariffs of 10 percent (i.e. multiplying all pre-existing tariffs by 1.1); and (iii) increasing all tariffs to 62.4%.<sup>16</sup> The aim of this exercise is to establish whether the trade-offs that may arise under protectionist scenarios are in general consistent with those derived when countries liberalize. Accordingly, we summarize the results in Table 10 which presents estimates of the (inequality adjusted) gains from trade, the number of countries that exhibit trade-offs, and the trade policy reversals. Country-specific estimates of the gains from trade are presented in Appendix F.<sup>17</sup>

> 15See Appendix E for details.

> 16Following Ossa (2014), this is the level of tariffs that would prevail if countries did not fear retaliation from the rest of the world.

> 17These country-specific losses/gains from trade under these various scenarios are not necessarily the mirror image of the results obtained when liberalizing. This is because the autarky scenarios may have

29
For each country-year we build three instruments. The first is the exogenous components of all FDI flows, regardless of their source. This is the main instrument used in the analysis. The second instrument only includes flows from advanced economies to emerging market and developing countries (“North-South” flows). The third instrument only includes flows among emerging markets and developing economies (“South-South” flows).<sup>12</sup> As we build more than one instrument for FDI, we can use multiple instruments in the same regression and then test for their validity with over-identifying restrictions tests.

A good instrument for FDI should only capture exogenous push factors driving FDI inflows. We think that our instrument meets this requirement because it only uses variables that are exogenous with respect to the recipient country. A more challenging requirement for a good instrument is that the instrument should not have a direct effect on GDP growth in the host country. One possible criticism of our instrument is that the factors that drive FDI inflows also drive trade. This would be the case if the main source countries for FDI are also the main trading partners of the host countries. To allay this concern, all our regressions control for trade. We discuss this potential violation in the next section. The third requirement for a good instrument is relevance. An instrument is relevant when there is a strong partial correlation between the instrument and the endogenous variable. Unlike the exclusion restrictions, relevance can be tested. To this aim, we report first stage F statistics on the instrument and a standard weak instrument test.

# **4.2 Cross-Country IV Regressions**

We start by estimating a set of cross-sectional regressions similar to those described before using _zit_ as the instrument for FDI. As in the previous cross-section analysis, we estimate the sample over different 20-year growth spells.

Since the bilateral data on FDI flows that are necessary to build our instrument are only available from 1990, we test if the time variation of the FDI coefficient emerges also in the IV regressions by estimating the model separately for five 20-year periods: 1990-2009; 1992-2011; 1994-2011; 1996-2015 and 1998-2017. As the sample of countries for which we are able to compute the instrument is different from that of the OLS regressions described before, we show the IV results together with OLS results estimated over the same country sample.

In the baseline regressions, we use an exactly identified model in which net FDI inflows are instrumented with the value of _zit_ computed using the exogenous components of all FDI flows, regardless of their source (Table 4 ).

IV regressions suggest that FDI is positively associated with economic growth when we estimate the model over 1990-2009 and 1992-2011 (columns 2 and 4 of Table 4). However, the FDI

> 12Appendix Figure C.1 shows the share of North-South and South-South FDI received by developing and emerging countries over our period of analysis.

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Policy Research Working Paper 10631

# **Abstract**

This paper aims to understand the existing gaps in microlevel data on forcibly displaced people—refugees and internally displaced persons. The paper undertakes a comprehensive review of all existing micro-level data sets in the United Nations High Commissioner for Refugees Microdata Library and the World Bank Microdata Library. It first identifies a corpus of micro-level data sets that are designed to have a representative sample of refugees and/or internally displaced persons and assesses gaps in geographical and thematic coverage. The paper then evaluates whether the data sets contain a core set of questions that are essential for the proper identification of refugees and internally displaced persons. The findings show that microdata on forcibly displaced people are comparatively rich in Sub-Saharan Africa in contrast to other regions. However, data scarcity is notably pronounced in countries facing fragility and conflict. Scarcity is also evident among internally displaced persons and on topics such as labor and employment, finance (for instance, credit, debt, and banking), agriculture/livestock/ fishery, and education. The paper also highlights that many of the existing micro-level data sets on forcibly displaced people do not contain the core set of questions needed for proper identification of refugees or internally displaced persons according to international statistical standards.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at tmasaki@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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._ Produced by the Research Support Team
# **4.  SRO Responsibilities**

This chapter reviews the types of regulatory responsibilities that SROs typically perform, and comments on the division of responsibilities between a SRO and its supervising regulator.

## **Nature of SRO Responsibilities**

SROs are usually responsible for specific market regulation and member regulation functions. Exchange SROs also regulate listed companies to varying degrees in their listing rules. The scope of an SRO’s responsibility varies in different countries. Invariably, responsibility is shared with the government authority, which also supervises the SRO. Government regulators are usually responsible for higher-level activities, while SROs are usually responsible for front-line activities.

Market regulation covers rules to protect market integrity, market conduct rules, and exchange trading rules. Compliance with those rules is monitored through market surveillance programs, trading analysis, and examinations of firms’ trading operations.

Member regulation refers to regulation of the operations of participants, securities dealers, or intermediaries. At the SRO level, it usually covers membership or access standards, rules on business conduct and sales practices, and financial compliance (including capital requirements). Compliance with those rules is monitored through compliance and financial examinations, financial and operations reporting to the SRO, and administration of membership rules.

SROs usually have responsibilities to investors, issuers, and the public imposed on them by law or regulation, and they are accountable to their supervising regulator. SROs are broadly accountable to stakeholders in the capital markets, not just to their members or shareholders. Those responsibilities require the SRO:

- to ensure compliance with its rules and, in some cases, securities regulations;

- to protect market integrity and investors by imposing rules on business conduct; and

- for SROs that regulate markets, to maintain a fair, efficient, and reputable public market.

Two recent international surveys have examined the division of responsibilities between government regulators and SROs. In 2004 the World Federation of Exchanges (WFE 2005) surveyed its members on their regulation responsibilities. The survey showed that a large majority of exchanges perform at least some market regulation. Many regulate trading members and supervise clearing and settlement activities. Many also perform a listings role and have some responsibility for listed company disclosure and corporate governance. The results show that each of the regulatory roles is usually shared with the government regulator.

In 2006, the International Council of Securities Associations (ICSA 2006b) conducted a survey of 10 major exchange and independent SROs. All the SROs surveyed regulate member firms. Almost all regulate markets, either exchange or OTC. The exchange SROs also regulated issuers.

32
(Kilby 1983; Biggs and Shah 2006). In addition, from a sociological point of view, an ethnic minority, which is characterized by common traits such as language, culture, and religion, generates trust, social cohesion, and emulation, which are all factors that favor entrepreneurial behavior (Greif 1993; Hobday 1995; Iyer and Schoar 2010). Finally, a minority group may be affected by a feeling of insecurity and frustration (in comparison with a dominant group), which encourages members to seek economic success and better social status (Elkan 1988).<sup>21</sup> Empirical evidence is generally consistent with the hypotheses just discussed. For instance, Ramachandran and Shah (1999), using firm-level data from Kenya, Tanzania, Zambia, and Zimbabwe and after controlling for firm size and age, various personal characteristics of the entrepreneurs, and sector and country differences, found that Asian and European firms start larger and grow faster than do indigenously owned African firms. Similarly, Hewitt and Wield (1997) show that Asian businesses in the Tanzanian manufacturing sector have better access to sources of technology than do indigenous companies. In the previously cited study by Goedhuys and Sleuwaegen (2000), the consistent finding is that the dummy variable “non-African” significantly and positively affects the likelihood of becoming an entrepreneur in Côte d’Ivoire. When analyzing a randomly selected sample of 296 Ethiopian SMEs, Mengistae (2001) finds that companies owned by the indigenous minority group of the Gurage perform better than average in the country; in particular, new businesses start larger and then grow faster. More recently, Goedhuys and Sleuwaegen (2010) show that the Asian dummy (equal to 1 for entrepreneurs of Lebanese, Indian, Middle Eastern, or other Asian origin) turns out to be positive and significant in affecting firms’ growth in Sub-Saharan Africa.

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discuss how real-time, smartphone data collection can reveal greater insights on individuals’ opportunity costs of time, along with a wider view of different activities they are involved in.

# 5.1 Intraday variation in time allocation

Across treatment arms, when looking at the intraday distribution of time across key activity categories (presented in Figure 5 by local polynomial smooth plots of the share of activities reported throughout the day),<sup>19</sup> we observe large differences in time allocation across such areas as agriculture, nonfarm employment, leisure, and unpaid work. While non-sleep, non-leisure activity reporting is much higher early in the day for the recall arm, there is a substantial drop-off in reporting for nearly all non-sleep activities by mid-day or early evening around 6 pm. The smartphone arm, on the other hand, reveals much more evenly distributed reporting, with drop-offs towards sleep only much later in the day (around 9 pm). The share of daily time in activities after 6pm accounts for about 26 percent for women and 29 percent for men the smartphone sample—compared to 17 and 19 percent for women and men in the recall sample, respectively—leading to a substantial loss of information when moving to recall-based methods. Furthermore, Table 9 highlights how time in activities such as unpaid work and non-farm employment begins to fall significantly in the recall arm vis-à-vis the smartphone arm after 12 pm, along with agriculture and leisure after 6 pm. Substantial amounts of daily time are spent by smartphone respondents in unpaid work and non-farm employment after 6 pm (17 and 33 minutes in nonfarm employment for women and men, respectively, and 83 and 17 minutes, respectively, in unpaid work). Although the recall arm tends to report higher time in leisure throughout the day, this trend flips after 6 pm as well. Men and women in the smartphone arm also spend nearly twice as much time in transport-related activities after 6 pm, compared to the recall arm. These trends are likely a reason for underreporting of participation across key activities in the recall vis-à-vis smartphone arm (Table 6).

> 19 The results are based on the pooled Day 5, 8 and 10 samples. 95 percent confidence intervals are included as well.

20