rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 10 VWAP spreads between foreign and domestic institutions: Investor style correlates**

This table reports the estimates of daily Value-Weighted-Average-Price (VWAP) of foreign investors relative to domestic institutions. The dependent variable in columns (1)-(4) is the VWAP for purchases. VWAP for sells is reported in columns (5)-(8). Each model include time and stock fixed-effects and t-statistics, presented in parenthesis, are calculated from standard errors clustered at the fund level. Note: ***/**/* indicate that the coefficient estimates are significantly different from zero at the 1%/5%/10% level.

|||Bu|ys|||Se|lls||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Firm-stock vari**|**ables**||||||||
|lsize|0.010|0.016|0.014|0.010|0.050***|0.045***|0.045***|0.044**|
||(0.459)|(0.718)|(0.657)|(0.466)|(3.045)|(2.834)|(2.810)|(2.477)|
|BMratio|-0.020|-0.020|-0.016|-0.013|0.044**|0.043**|0.038*|0.024|
||(-1.019)|(-0.980)|(-0.757)|(-0.661)|(2.401)|(2.367)|(1.913)|(1.111)|
|ctc5|-0.516***|-0.494***|-0.543***|-0.551***|-0.384***|-0.380***|-0.383***|-0.388***|
||(-4.915)|(-4.872)|(-5.123)|(-5.025)|(-4.342)|(-4.297)|(-4.129)|(-4.022)|
|cto|1.312***|1.352***|1.441***|1.460***|1.354***|1.347***|1.541***|1.324**|
||(2.940)|(3.021)|(3.071)|(3.121)|(2.678)|(2.663)|(2.834)|(2.454)|
|otc|8.127***|8.159***|8.528***|8.487***|8.266***|8.275***|8.727***|9.107***|
||(8.304)|(8.307)|(8.189)|(8.165)|(7.182)|(7.169)|(7.136)|(7.098)|
|avgps|-10.197|-10.363|-10.153|-9.536|-19.286***|-18.294***|-18.037***|-20.192***|
||(-1.151)|(-1.180)|(-1.164)|(-1.059)|(-4.299)|(-4.155)|(-4.124)|(-4.898)|
|**Investor variabl**|**es**||||||||
|ActiveShare|-0.173***|-0.105***|-0.093***|-0.121***|0.097***|0.055***|0.043**|0.108***|
||(-7.509)|(-3.647)|(-3.556)|(-5.892)|(4.730)|(2.666)|(2.029)|(5.260)|
|Diferent Stocks||0.006***|0.006***|||-0.004***|-0.004***||
|||(4.979)|(5.343)|||(-3.698)|(-3.520)||
|Turnover|||-0.009***|-0.009***|||0.007**|0.010**|
||||(-2.974)|(-2.950)|||(2.347)|(2.463)|
|Net Flows|||0.013**|0.014*|||-0.003*|-0.001*|
||||(-2.282)|(-1.962)|||(-1.819)|(1.772)|
|Trading Interval||||0.079***<br>(8.659)||||-0.030***<br>(2.793)|
|Constant|-0.100|-0.331|-0.299|-0.229|-1.658***|-1.507***|-1.485***|-1.487***|
||(-0.153)|(-0.508)|(-0.464)|(-0.359)|(-3.435)|(-3.226)|(-3.186)|(-2.857)|
|Observations|104,321|104,321|101,178|101,178|71,471|71,471|69,034|64,906|
|R-squared|0.126|0.127|0.133|0.133|0.137|0.138|0.144|0.137|
|FE|day|day|day|day|day|day|day|day|
||stock|stock|stock|stock|stock|stock|stock|stock|
|Cluster|Investor|Investor|Investor|Investor|Investor|Investor|Investor|Investor|

35
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Varga, J. 1995. "Returns to Education in Hungary." _Acta Oeconomica_ 47 (1-2): 201-216.

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- Wei, X., M.C. Tsang, W. Wu and L.-K. Chen. 1999. "Education and Earning in Rural China." _Education Economics_ 7(2).

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- Weisbrod, B.A. 1964. _External Benefits of Education._ Princeton University, Industrial Relations Section.

Welch, F. 1970. "Education in Production." _Journal of Political Economy_ 78(1): 35-59.

- World Bank. 1996a. _Republic of Ghana: Basic Education Sector Improvement Program_ (Report No. 15570-GH). Washington, D.C.: World Bank.

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- World Bank. 1998. _Ethiopia: Education Sector Development Program_ (Report No. 17739-ET). Washington, D.C.: World Bank.

**Table 1. Returns to Investment in Education by Level, Full Method, Latest Year, Regional Averages (Percentage)**

|||Social|||Private||
|---|---|---|---|---|---|---|
|Region|Primary|Secondary|Higher|Primary S|econdary|Higher|
|Asia|16.2|11.1|11.0|20.0|15.8|18.2|
|Europe/MiddleEast/NorthAfrica|15.6|9.7|9.9|13.8|13.6|18.8|
|LatinAmerica/Caribbean|17.4|12.9|12.3|26.6|17.0|19.5|
|OECD|8.5|9.4|8.5|13.4|11.3|11.6|
|Sub-SaharanAfrica|25.4|18.4|11.3|37.6|24.6|27.8|

12
<!-- Start of picture text --> Low income 30.5<br>Average across all<br>countries: 30.7%<br>Lower middle income ed<br>Upper middle income 34.3<br>incomeaveragelevel A<br>High income 28:2!<br>0 25 50 75 100<br>SDG Gender Indicator Availability<br>The 25th percentile of the SDG gender availability is 26%. The median is 32% and the 75th percentile is 38%. Means are<br>unweighted. Circles are sized according to population<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>y = 45.76 + -3.20 x, R2 = 0.03<br>& (5.58) (1.33)<br>n<br>5 75<br>o<br>2<br>To<br>£<br>[U}<br>B<br>aa@ °° °@ @ e re)@ % @<br>B © °8 8, & ee @¢ * e<br>2 Pf © ° © ° % Fog 8 66 Pog<br>><br>ns ee oe Eas — oath ng og<br>Fa o ° °° %e © oo f°, ee © ©<br>@= 2 @ orn)@ @ Q @©e@ e @ ° @ @<br>S ° eo ° @<br>< -e°©© eto) ©<br>@@<br>()<br>1,000 10,000 100,000<br>GDP per capita, PPP (current international $), logged<br>region9 . @@ EastEuropeAsia & CentralAsia& Pacific @@ LatinMiddleAmerica East & North& CaribbeanAfrica @@ SouthNorthAmerica Asia @ Sub-Saharan Africa<br>Source: GDP per capita from the World Development Indicatrs (NY.GDP.PCAP.PP.CD). The availability of<br>Gender SDG Indicators based on authors calculations based on the UN Global SDG Database.<br><!-- End of picture text -->
|**Table 3: Summary statistics for Female‐owne**|**d enterpr**<br>**Total**|**ises ‐ Kass**<br>**StDev**|**ida data set**<br>**Crossover**|**Non‐Crossover**|**Difference**|
|---|---|---|---|---|---|
|**_Financials_**||||||
|Revenuespast month(UGX '000)|1,058|2,246|2,697|914|1,783***|
|Profitspast month(UGX '000)|229|392|494|205|289***|
|**_Investment_**||||||
|Net capital invested(UGX '000)|2,056|5,880|5,312|1,769|3,543***|
|Number of workers|2.6|2.0|3.9|2.4|1.5***|
|Inputs consumedpast month(UGX '000)|377|783|968|325|643***|
|**_Personal characteristics_**||||||
|Age|37|9|42|36|6***|
|Married|53|50|40|54|‐14|
|Widow|13|34|32|11|21***|
|Less thanprimaryeducation|12|32|30|10|20***|
|Primaryschool is max education|52|50|26|54|‐28***|
|Secondaryschool is max education|24|43|30|24|7|
|Completed more than secondary|13|33|13|13|0|
|Previouslyworker in sector|13|33|12|13|‐1|
|Received business or technical traininginpast|43|50|72|40|32***|
|Financial literacyindex(0‐1)|0.5|0.1|0.5|0.4|0.1***|
|Technical literacyindex(0‐1)|0.4|0.2|0.4|0.4|0.0|
|Owns more than one business|15|36|24|14|10|
|Household and business activities simultaneously|30|46|44|29|15|
|**_Business characteristics_**||||||
|Business owner started this firm|93|26|84|93|‐9*|
|Saw market opportunitywhen startingbusiness|56|50|48|57|‐9|
|Has businesspartners|3|16|0|3|‐3|
|Business is registered|46|50|44|46|‐2|
|Age of business|9|7|14|9|5***|
|Size of business network(log)|3.2|1.1|3.4|3.1|0.2|
|**_Access tofinance_**||||||
|Has bank account|51|50|60|50|10|
|Has received business support|14|18|19|13|6*|
|Belongs to SACCO|18|39|32|17|15*|
|Has received loan|32|47|44|31|13|
|**_Harassment(past 12 months)_**||||||
|Has been sexuallyharassed|16|37|8|17|‐9|
|Other type of harassment+|42|49|40|42|‐2|
|N|309||25|284||

note: *** p<0.01, ** p<0.05, * p<0.1. At the time of the survey, 1 US$ = UGX 2,250.

+ other type of harassment includes: threats to shut down business; shouting, scolding, making a nuisance in or near enterprise premises; vandalism of premises or merchandise; confiscation of property or merchandise; pushing or shoving; and beating.

Table 3 shows the vast difference across these two types of female entrepreneurs and their firms. First of all, we can see that the average revenue of a crossover firm is about three times that of a non‐ crossover firm, and profit is more than double. In addition, as might be expected, the crossover firms 10
analysis showed that social and emotional skills immediately after experiencing the SEL program improved academic performance, reduce emotional distress, and conduct problems (Taylor et al., 2017).

Effective teachers use techniques and strategies designed to improve children’s socioemotional development. In doing so, they benefit children’s understanding of the people in the world around them as well as support children’s ability to understand and work with people who are different from them (CASEL, 2018; Elbertson, Brackett, & Weissberg, 2010). Effective teachers use strategies that develop students’ ability to make responsible decisions related to learning, encourage students’ self-awareness and self-management, and equip them with interpersonal skills to work effectively with others.

Considerable disagreement arises in how people name, define, and describe these skills; common terms include noncognitive skills, 21<sup>st</sup> century skills, and soft skills (National Research Council, 2012). Jones & Bouffard (2012) describe three key categories of socioemotional skills: emotional processes, cognitive regulation, and social and interpersonal skills. Emotional processes refer to students’ knowledge, awareness, and management of emotion. Cognitive regulation involves students’ management of their attention and inhibition of inappropriate responses. Social and interpersonal skills involve interpreting others’ behaviors and interacting in positive ways with other people. _Teach_ measures the ways that teachers cultivate these skills. Specifically, _Teach_ measures how teachers support student autonomy (which implicates students’ cognitive regulation skills), perseverance (which exercises students’ emotional processes and cognitive regulation), and social and collaborative skills (which requires students’ emotional processes and interpersonal skills).

# **3.4.1. Autonomy**

Autonomy support is defined as “the instructional effort to involve, nurture, and develop students’ inner motivational resources and capacity and responsibility for selfmotivation” (Reeve, 2009, p. 168). Effective teachers foster autonomy in the classroom by creating opportunities for students to take ownership of their own learning by building instruction around students’ interests, preferences, and choices (Evans & Boucher, 2015; Katz & Assor, 2007). Teacher practices that support autonomy nurture students’ internal motivation to learn, rather than relying on external sources of motivation such as rewards, consequences, and deadlines (Reeve, 2009). Not all student choice is effective, however 28
of the analysis is to understand household preferences for REDD+ contracts and to estimate the cost to rural Ethiopian households of taking on fuelwood collection and grazing reduction obligations to sequester carbon. We also seek to shed light on respondents’ preferences for REDD+ institutional arrangements, such as the portion of payments going to communities versus households and the terms of REDD+ contracts.

We focus on Ethiopia because it presents an ideal case study to answer the above questions. In Ethiopia population pressure has led to the expansion of agricultural activities, and unsustainable consumption of fuel wood and the country’s forests are being lost at a rapid rate. Livestock grazing and to a lesser extent, illegal logging and forest fires also contribute to these problems. Though Ethiopia is not a major emitter of greenhouse gases, it has a significant potential for climate change mitigation. It is believed that the country’s participation in REDD+ will generate many benefits to the sustainable management of forests and the rural people who depend on natural resources for their livelihood.

Ethiopia has committed to developing REDD+; the government submitted a REDD+ Program Idea Note (R-PIN) in 2008, a final REDD+ Preparation Proposal (R-PP), and Climate Resilient Green Economy (CRGE) strategy in 2011 with the objective of reaching carbon neutral economy by 2025 and established a REDD Secretariat in 2013 under the Ministry of Environment and Forest.

We use a choice experiment survey, which allows us to analyze preferences for individual attributes and to calculate part-worth utilities. In addition, we use an experimental design that includes attribute interaction terms to analyze the trade-offs between attributes. Finally, we analyze the robustness of the results by using the “WTP-Space” approach in addition to the standard “Utility-Space” approach to calculate the marginal values for attributes. The data 4
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38
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1.29 Tuie rest of the decade saw a proliferation of agriculturally-related parastatels that differed in size, comprehensiveness, geographic scope, crop and economic orientation, and even as to the ministry to which they were responsible. Some parastatals were region-specific integrated development efforts that covered infrastructure, rural health, and other sectors, as well as agriculture; others were commercially oriented concerns which focused attention on a single export crop; and still others were organizations with a national mandate to improve specific agricultural services like credit, food crop marketing, agricultural machinery. However, whatever their implementation orientation, each parastatal established whatever research, extension, or field level services they deemed necessary.

# Phase 3: Push for Government-Led Integration

1.30 In preparation for the Fifth Five-Year Plan, HINAGRI commissioned a thorough internal review of its activities and Cameroonian agricultural policy more generally. The resulting Bilan Diagnostic provided a frank, but gloomy, assessment of the situation. In the extension area, the study concluded that: The staff joining the extension service, particularly at the lowest levels, were receiving inadequate pre-service training;

- -nThe training that was being offered was too theoretic and did not apply to the issues and problems faced in the field;

- In-service training was insufficient at all levels and often inappropriate;

- There were few incentives for field work, even for extension agents, who lacked transportation and supervision. Consequently, there was almost no contact between the extension service and the farmers;

- There was little contact with agricultural research or coordination with other rural development services;

- There were regional imbalances in the ratios of extension agents to farmers.

Overall, the study noted overlapping jurisdiction between the government and parastatals often resulting in a duplication of services and concluded that MINAGRI was loosing its ability to coordinate agricultural policy, much less intervene directly.

1.31 Since the release of the Bilan, there has been a resurgence of the governments role in agricultur&. affairs that continues to this day. The move to greater direct government supervision of extension activities has received the support of donors, particularly the World Bank, and more recently, FAO and USAID. In the early 1980s the World Bank fundei several studies (Falloux, 1983; Bolinger and Nanko, 1985) intended to provide the foundation for a national extension project. These studies were supplemented by a national extension seminar held in December 1985 to gain input from the full range of those
The middle panel of Table 3 also reports the same diagnostic statistics for selected specifications from RST. In all of these specifications, the first-stage regressions perform well: seven of the eight first-stage F-statistics are larger than 10, and even the smallest is still respectable at 8. However, these F-statistics are not sufficient to diagnose the strength of the instruments. Closer inspection of the first-stage regressions shows the same tell-tale signs of potential problems as in the DK specification in Table 2. In seven of the eight first-stage regressions in Table 3, the instrument for institutions (settler mortality) has strong explanatory power for both institutions and trade, while the instrument for trade (the FR constructed trade share) is a significant predictor of both institutions and trade at the 5% level in seven of the eight specifications as well.

Table 3 also presents the same diagnostic statistics on instrument quality as in Table 2. The Shea partial R-squared statistics are small in most cases, although not as extremely so as in the basic DK specification. In four of the eight first-stage regressions, the partial R-squareds are less than 0.1, and in seven out of eight they are less than 0.2. More formally, in each of the four specifications, we cannot reject the null hypothesis that the instruments are weak. However, the size of the set of weak instruments under the null is different in the four specifications. In specifications 2 and 4, the minimum eigenvalues of G are 1.95 and 3.24, and we cannot reject even the most conservative set of weak instruments tabulated in Stock and Yogo (2001). In specifications 1 and 3, the minimum eigenvalues of G are 6.2 and 6.8, and we cannot reject the null that the instruments fall in the larger set of weak instruments that would lead to size distortions of at least 5 percent, i.e. a test with nominal size of 5% would in fact have a true size of 10%.

These results suggest that there are reasons to question inferences about the importance of trade and institutions based on conventional t-statistics in the main specifications considered by RST as well. In the next section of the paper we discuss approaches to inference that are valid even when the instruments are weak, and show that the confidence intervals for the parameters of interest are much larger than simple t- statistics would suggest.

10
# **Figure 7 Employment costs as a share of revenues by fully owned and partially privatized SOEs, by sector**

<!-- Start of picture text --> 200<br>188<br>150<br>110<br>100<br>72<br>50<br>18 21 19<br>15<br>0<br>Power Airlines and airports Roads Railways<br>Partially privatized SOEs Fully owned SOEs<br>Employment costs as percent of revenues<br><!-- End of picture text --> _Source_ : Authors, based on data from the World Bank Infrastructure SOEs Database. _Note_ : Data are for 2009–18. 0 denotes partially privatized firms and 1 denotes fully owned SOEs.

# **5 Comparison with Private Companies**

This section presents the results of a matching exercise that compares the performance and fiscal risk of SOEs with that of private firms.

# **_5.1 Data and Methodology for Matching Comparisons_**

To compare the SOEs with private firms, we follow a multistep process. First, we use the tools in Standard & Poor’s Capital IQ to create a comparison set. We search for firms in the same sector and same region, restricting the search to firms that have similar total assets in US dollars. In particular, we compile financials for firms that are within +/–20 percent of the assets of the firms in our sample as well as in the same industry and region. We end up with a comparison set of 10–20 firms for each of the SOEs in our database.

Second, we eliminate companies that are easily identifiable as SOEs in the comparison set, leaving a sample of over 600 firms to use in the matching exercise. We focus on the period 2009– 18, for which the World Bank Infrastructure SOEs Database has better coverage.

One concern we had was whether our database of private firms included state-owned firms, despite our efforts to try to eliminate them. Because Capital IQ does not provide information on the ultimate ownership of firms, in a first iteration we use their “public ownership” indicator to remove some obvious SOEs from the comparison set. We used the database of Lazzarini and 14
14

# **Figure 3**

## **Growth Rates of GDP per Capita and Household Consumption per Capita in Tanzania, National Accounts Data**

<!-- Start of picture text --> 5.0%<br>4.0%<br>3.0%<br>2.0%<br>GDP per Capita<br>1.0% Hhold Cons. per Capita<br>(National Account Data)<br>0.0%<br>-1.0%<br>-2.0%<br>-3.0%<br>1988 1990 1992 1994 1996 1998 2000 2002 2004<br>Year<br>Growth Rate<br><!-- End of picture text -->

# **4.4 Sources of Error in the Simulation Analysis**

Several sources of error enter the simulation analysis. First, there is the usual sampling error associated with the survey data. Second, there is uncertainty associated with the national accounts growth estimates. Third, there is drift between the national accounts GDP levels and household consumption levels, i.e. the growth rates of the two may not be equal. Fourth, the true year-by-year changes in the distribution of consumption differ from the changes assumed in the simulations. Fifth, the uniform application of national population growth rates to the rural and urban sectors appears inconsistent and sector specific growth rates might be preferable. Of all of these, only sampling error is readily quantifiable, and the associated standard error terms are calculated for all simulated poverty rates. It is important to recognize, however, that these standard errors are only an extreme lower bound for the true error. The fifth source of error, finally, is considered in Section 5.4.

# **5 Simulation Analysis**

The simulation analysis is conducted under a variety of scenarios, with varying assumptions, first using the Datt and Walker approach and then using the two survey approach. The intent is both to explore the sensitivity of the results to the underlying assumptions applied, and to identify variations which most closely match the change in the distribution of consumption observed between the two surveys.