rafmacalaba/datause-encoder-data huggingface dataset:
It is based on the model ��,� = ��,����,� + ��,� where ��,� is a trade direction indicator, ��,� is the transaction cost measure, and ��,� is an error term, all for stock _i_ at time t. Roll’s measure (Roll’s C) is derived under the assumption that buyer- and seller-initiated trades are equally likely. The Bayesian estimation of this cost measure using the Gibbs sampler is described in detail in Hasbrouck (2009). The higher the Roll’s C, the more illiquid the stock is.

The third liquidity measure captures the proportion of trading days with zero returns. The frequency of zero-return days as a liquidity proxy was introduced by Lesmond, Ogden, and Trzcinka (1999). We compute the proportion of days with zero returns for each stock within each year to estimate Zerofreq. The higher the Zerofreq, the more illiquid the stock is. To reduce potential endogeneity arising from contemporaneous measurement and to smooth out idiosyncratic changes, we use the 12-month moving average of each liquidity measure in our analyses.

Table 1 reports summary statistics for stock and investor characteristics for the US.<sup>8</sup> Only stocks that are traded by households in the data set are considered. Summary statistics are calculated by pooling annual observations over 1991-1996. The results show that our major transaction costs measure – adjusted Amihud ratio (AdjIlliq) - is positively skewed with a mean of 3.30, but a much smaller median of 0.46. The distributions of other transaction costs measures are similarly positively skewed as Roll’s C has a mean of 1.73 and a median of 1.06 and Zerofreq has a mean of 4.82% and a median of 2.70%. Size is also positively skewed, with the average market capitalization almost 8 times as large as the median firm size.

The mean (median) US individual investor’s portfolio value is $80,342 ($22,952) for the transactions analyzed in this study. 10% of the primary US account holders for the transactions

> 8 We report stock and investor characteristics for Finland in Table 7 and discuss the analyses for Finland in Section 6.

13
<!-- Start of picture text --> Probability of Stunting by Wealth Quintiles and E.Coli Contamination at Source<br>Specification (2.1e)<br>4<br>ow ©<br>c<br>5<br>c<br>Eat<br>n<br>—<br>°<br>Ec<br>5<br>©<br>SN<br>hk<br>a<br>(o)<br>0) 5 1 1.5 2 2.5 3 3.5 4 4.5 5<br>Log of Child Age<br>+ Low Contamination; Poorest Quintile ©'————4_ Low Contamination; Richest Quintile<br>-——1 Some Contamination; Poorest Quintile H—————41_ Some Contamination; Richest Quintile<br><!-- End of picture text --> <!-- Start of picture text --> Probability of Stunting by Wealth Quintiles and E.Coli Contamination at Source<br>Specification (2.2e)<br>4<br>wo ©<br>c<br>5<br>iS<br>3 ©<br>n<br>—_<br>°<br>Es<br>5<br>©<br>oNhk<br>a<br>(o)<br>0) 5 1 1.5 2 2.5 3 3.5 4 4.5 5<br>Log of Child Age<br>/——_ Low/Med Contamination; Poorest Quintile -———_ Low/Med Contamination; Richest Quintile<br>-————1_ High/V. High Contamination; Poorest Quintile -—————4_ High/V. High Contamination; Richest Quintile<br><!-- End of picture text -->
# 3.1 Response Bias

Bias can arise from the respondent's desire to be accepted by the enumerator and his peers. Numerous studies in the social sciences oer methods for reducing its associated measurement error (King and Bruner, 2000). Recent work demonstrates the fallibility of using social desirability indexes as an explanatory variable, a commonly practiced technique to reduce response bias. Norwood and Lusk (2011) conceptually show when the marginal cost of exhibiting social desirable behavior is zero, as in such hypothetical contexts, the behavior of individuals can be similar despite varying social desirability index values. Inference from hypothetical and non-hypothetical choice experiments corroborates their prediction: correlations between the social desirability index and socially desired behavior are greater for non-hypothetical choices.

To gauge the extent of response bias in our subjective knowledge measures, we added a placebo technique to the list of SLM techniques surveyed (planting in squares).<sup>9</sup> Since this technique is ctitious, no farmer should know it by memory. Hence we consider the 0.2 percent of men and women who knew the placebo outcome by memory to be routing errors. Taking these errors out of our know by name measure<sup>10</sup> implies that only 0.3 percent of men and 0.2 percent of women assert knowing the placebo technique by name, as prompted by the enumerator. The small magnitude observed suggests that any inference derived from our self-reported, subjective measure of knowledge should be broadly immune to response bias. In addition, the dierences in response bias by gender are not statistically dierent.

Using the same logic as above, response bias is not implicit in our self-reported adoption outcomes. A negligible portion of the sample reported adopting the placebo (6 individuals). Thus, knowledge and adoption responses to the placebo imply misreporting is not driven by a respondent's tendency to over-report to receive peer approval.

> 9Although we did not randomize the placement of the placebo in the list, the placebo technique was listed as the sixth of nine techniques. How the list is read will depend on which techniques the farmer recalled by memory, so technique placement on the list varies for each individualalbeit non-randomly.

> 10Another way to think about misreporting would be to remove these routing errors from the denominator when computing the incidence of misreporting. This does not substantially change the results.

9
_H4: Business owners with higher ‘financial’ managerial capital will increase firm profits to a greater extent when these owners are also operating more established firms._

# **3. Sample Selection and Research Design**

We test the four hypotheses developed in the previous section in a sample of 852 small businesses in the Cape Town area of South Africa through a randomized control trial. The study design comprises two treatment arms, with 266 businesses randomly assigned to finance training and 270 businesses to marketing training. A third group of 316 businesses, the control arm, did not receive any training but was surveyed in the same manner as the treatment groups at baseline and followups. Businesses in the control arm were promised (and provided) a business training course in 18 months once the study period was over, in order to retain participation in all surveys.

# **3.1. Sample Selection and Timeline**

Businesses were selected into the study sample using a three-stage process. In stage one, using a systematic and geographically exhaustive sampling plan, a team of 12 research administrators (RAs) worked for ten weeks starting in July of 2012 to approach approximately 10,000 businesses in the greater Cape Town area. The only requirement for recruitment at this stage was that the businesses had to be operating out of a physical structure (e.g., small shop, shipping container, or larger retail space). The RAs were instructed to exclude businesses operating in mobile street stands, roadside carts, or other non-permanent structures. Each entrepreneur approached was given a sales pitch for our business training program and the opportunity to apply for the program by participating in a short screening survey conducted by the RA. Through this process, 2,168 screening surveys were obtained. Next, the research manager and field coordinators examined basic financial and operating questions, as well as open-ended text responses describing the business and its customers and products, to assess whether a firm was in fact operational and running a business in which money exchanges hands (i.e., real customers currently pay for the products/services). A total of 116 observations were dropped because the businesses were nonoperational and another 101 observations were dropped due to duplicate entries, missing data, or inconsistent responses (e.g., person signed up was not a firm owner). Our sampling frame therefore 12
**Table 6c Relative trade gains of European partner countries (relative to their respective total trade volume) resulted from regulatory policy improvement (half-way to EU15) of each new member and candidate member countries on their exports and imports**

|Regulatory<br>policy||EU15||N|ewmembe|rs|Candida|temember|countries|
|---|---|---|---|---|---|---|---|---|---|
||Exportgain|Importgain|Tradegains|Exportgain|Importgain|Tradegains|Exportgain|Importgain|Tradegains|
|Czech||||||||||
|Republic|3.49|8.07|5.70|12.48|31.41|22.75|1.86|7.11|4.71|
|Estonia|0.14|0.30|0.22|0.40|0.77|0.60|0.07|0.02|0.05|
|Hungary|3.97|10.33|7.04|8.85|17.94|13.75|5.24|15.03|10.55|
|Latvia|0.19|0.31|0.25|1.28|1.41|1.35|0.13|0.01|0.06|
|Lithuania|0.21|0.30|0.25|1.17|0.99|1.07|0.11|0.51|0.33|
|Poland|7.49|12.95|10.12|19.55|34.54|27.32|6.34|10.85|8.79|
|Slovakia|1.27|2.90|2.06|9.31|17.44|13.66|0.79|3.39|2.20|
|Slovenia|0.88|1.74|1.30|1.83|3.58|2.77|0.73|1.75|1.28|
|Bulgaria|0.34|0.76|0.55|0.67|0.61|0.64|1.54|3.10|2.38|
|Romania|1.61|3.48|2.51|4.09|3.19|3.61|4.96|8.40|6.80|
|Turkey|5.98|14.04|9.87|3.81|10.48|7.37|24.43|43.34|35.24|

Note: Figures are presented in 1/10000. For example, if the regulatory policy development level of the Czech Republic reaches halfway of EU15 average, the export volume of EU15 will increase 0.000349.

**Table 6d Relative trade gains of European partner countries (relative to their respective total trade volume) resulted from IT infrastructure improvement (half-way to EU15) of each new member and candidate member countries**

|IT<br>infrastructure||EU15||N|ewmembe|rs|Candida|temember|countries|
|---|---|---|---|---|---|---|---|---|---|
||Exportgain|Importgain|Tradegains|Exportgain|Importgain|Tradegains|Exportgain|Importgain|Tradegains|
|Czech||||||||||
|Republic|4.11|11.49|7.67|14.71|44.73|30.99|2.19|10.13|6.50|
|Estonia|NA|NA|NA|NA|NA|NA|NA|NA|NA|
|Hungary|9.00|28.27|18.30|20.06|49.10|35.72|11.87|41.12|27.73|
|Latvia|0.94|1.83|1.37|6.33|8.44|7.45|0.65|0.05|0.32|
|Lithuania|2.13|3.66|2.87|11.92|12.15|12.04|1.12|6.25|3.90|
|Poland|18.78|39.25|28.66|49.04|104.69|77.90|15.91|32.90|25.12|
|Slovakia|2.10|5.77|3.87|15.33|34.68|25.69|1.29|6.75|4.25|
|Slovenia|1.60|3.80|2.66|3.31|7.82|5.73|1.31|3.82|2.67|
|Bulgaria|1.93|5.18|3.50|3.74|4.15|3.96|8.67|21.00|15.27|
|Romania|3.11|8.14|5.54|7.92|7.47|7.68|9.61|19.67|15.01|
|Turkey|8.93|25.34|16.85|5.69|18.92|12.75|36.50|78.23|60.37|

Note: Figures are presented in 1/10000. For example, if the IT infrastructure development level of the Czech Republic reaches half-way of EU15 average, the export volume of EU15 will increase 0.000411.

23
# CONTENTS

|I.|INTRODUCTION ...............................|. l1|
|---|---|---|
|II.|PLANNING THE VISIT .........................|........ L|
|III.|VISITING VILLAGES ..........................|.......<br>3|
|IV.|VISITING HEALTH WORKERS ....................|.............<br>8|
|V.|VISITING NUTRITION WORKERS .................|...............<br>11|
|VI.|MAKING USE OF THE PROCESS ..................|..............<br>15|

# ANNTEX

SAMPLE FIELD VISIT REPORT ........................ .......... 18
The gap in living standards correlates in part to personal characteristics of residents such as ethnicity and education level. In particular, it is recognized that poverty in Vietnam is strongly associated with ethnicity (ADB, 2006; Epprecht, Müller, and Minot, 2011; Hung and Reilly, 2009).<sup>27</sup> Table 2 shows that minorities are especially concentrated in the mountainous regions. Educational attainments varied across locations; urban dwellers tended to be better educated with 8.7 years of educational attainment on average as compared to those in rural areas with 6.6 years of education.

The regional disparities can also be viewed in terms of employment opportunities. Whereas the unemployment rate in Vietnam for the year 2004 was relatively low, underemployment is a common phenomenon in Vietnam especially in rural areas.<sup>28</sup> Foreign- and state-owned sectors may provide superior jobs, offering workers full-time employment opportunities and paying higher wages given personal characteristics (Fukase, 2013a). Table 2 demonstrates that the proportions of workers who are employed in state and foreign sectors relative to total workers are 10.1 percent and 2.2 percent respectively. Employment in both sectors is nearly four times higher in urban areas relative to rural areas. As one might expect, state employment is highest in proportion in Hanoi (32.7 percent). As seen in Figure 4, employment by foreign firms is particularly unevenly distributed across regions with its highest concentration to be found in the Southeast.

# **V. First-Stage Model of the Determinants of Out-migration**

A multinomial logit model (Greene, 2003, Chapter 21.7.1) is a useful tool to examine how the determinants of migration differ according to the four migration outcomes defined above. The VMS includes information about the province from which each migrant originated and whether

> 27 While only 13.5 percent of the ethnic Vietnamese (Kinh) and Chinese were poor, 60.7 percent of ethnic minorities lived below the poverty line in 2004 (ADB, 2006). See Hung and Reilly (2009) for analyses of ethnic wage gap. 28 Rural workers of working age worked only about 79 percent of working hours in 2004 (GSO, 2006, p.57).
Policy Research Working Paper 9263

# **Abstract**

Using a randomized survey experiment in urban Ghana, this paper demonstrates that the length of the reference period and the interview modality (in person or over the phone) affect how people respond in labor surveys, with impacts varying markedly by job type. Survey participants report significantly more self-employment spells when the reference period is shorter than the traditional one week, with the impacts concentrated among those in home-based and mobile self-employment. In contrast, there is no impact of the reference period on the incidence of wage employment. The wage employed report working fewer days and hours when confronted with a shorter reference period. Finally, interviews conducted on the phone yield lower estimates of employment, hours worked, and days worked among the self-employed who are working from home or a mobile location as compared with in-person interviews.

This paper is a product of the Development Research Group, Development Economics and 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 brijkers@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
# **Figure 4: The working-age population could become more skilled due to demographic change, if educational attainment rates are constant**

Share of working-age population with at least 9 years of schooling if educational attainment rates constant (percent) <!-- Start of picture text --> 2015 2050<br>High-income OECD<br>Upper-middle-income countries<br>Lower-middle-income<br>Swaziland<br>South Africa<br>Namibia<br>Lesotho<br>Botswana<br>15 25 35 45 55 65 75 85<br><!-- End of picture text --> Source: Authors’ estimates Note: Data from UN (2015) and GMD household surveys. The data for upper-middle-income countries and high-income OECD are simple averages. Projections presented for SACU countries and two potential educational attainment convergence targets. High income OECD, upper-middle income, and lower-middle income country groupings are based on the World Bank Group’s income based country classification.

While an understanding of the mechanisms and demographic trends can provide a sense of how policy impacts may differ across the region, a purely analytical approach may not provide any insight on their relative magnitudes, possibly confounding policy priority setting. These assessments are additionally complicated by the fact that the demographic changes can induce general equilibrium effects whose magnitude cannot be clearly identified in a multi-agent and multi-sector economy. However, widely cited work on the growth impact of changes in agestructure generally rely on empirical estimation of growth accounting relationships.<sup>6</sup> These provide some robust estimates of the importance of demographic changes on income per capita and savings, but do face some constraints. For example, since these empirical studies rely on historical variation in data to drive results, the studies may not necessarily find statistically significant impacts on economic indicators, even if the intuition would suggest otherwise. This may be why early empirical work for Africa, as in Bloom and Sachs (1998), found no significant impact of demographic change on growth. More recently, Canning, Raja, and Yazbeck (2015) finds that changes in age-structure have no discernable impact on savings as a share of GDP for African economies.

For this reason, simulation models have proven to be a useful approach to examine hypothetical impacts of future demographic shifts on economic outcomes. For example, Ashraf, Weil and Wilde (2013) presents a simulation model of economic growth and demography, where real GDP

> 6 This vast literature includes Bloom and Williamson (1998), (Higgins and Williamson (1997), Eastwood and Lipton (2011), Kelly and Schmidt (1995, 2005, 2007), _inter alia_ .

8
in Ghana, one of the higher-income countries in the region, only 56 percent of adults use any kind of financial product. This figure rises to 81 percent in Lesotho, but falls to just 22 percent in Mozambique. It is interesting to note that Pakistan has a very similar access profile to Tanzania; both countries have high levels of financial exclusion, and use of informal products is about three times more prevalent than use of formal products. Awareness of basic financial products and concepts vary from country to country as well, but is generally also low, with many people never having heard of savings accounts. However, the financial literacy data from the FinScope surveys is limited in that it generally focuses only on awareness of financial products and providers, and not on other dimensions of financial literacy, such as numeracy or capability.

Other national financial literacy surveys have been conducted in the U.K. (Atkinson et al., 2007), Austria (Fessler et al. 2007), Australia (Worthington, 2004), Poland (Szafranska and Matysik-Pejas, 2010), Singapore (Media Research Consultants, 2005), Fiji (Sibley, 2010), and Ireland (O’Donnell and Keeney, 2009), although many of these tend to focus on broader measures of financial capability. The specific results from these surveys are not presented here due to lack of direct comparability.

The comparable surveys find that financial literacy is low everywhere, though still lower in low-income countries. Lusardi and Mitchell (2011b) find in the U.S. that only about 65 percent of respondents correctly answered the first question or the second question, while only half gave the correct response to the third question. Table 1 shows that these scores are fairly comparable to those in other high income countries. For instance, Sekita (2011) finds that slightly worse performance on the financial literacy test for individuals in Japan—the correct response rate on the first two questions is 50 percent, and 40 percent on the last question. In Sweden, Almenberg and Säve-Söderbergh (2011) find that the correct answer rate for the second two questions (60-70 percent) is higher than for the first (35 percent), but this is because a much more difficult variation of the first question is asked.

6
<!-- Start of picture text --> 0.005 Interest Rate 0.01 Maturity<br>0<br>“0.015<br>-0.005<br>-0.01 -0.02<br>“0.015<br>-0.025<br>-0.02<br>-0.025 -0.03<br><!-- End of picture text --> <!-- Start of picture text -->
-0.03<br>-0.035<br>~0.035<br>-0.04 -0.04<br>0 2 4 6 8 10 12 0 2 4 6 8 10 12<br>Quarters since commisioner appointment Quarters since commisioner appointment<br>63 Collateral = «10% Default<br>0.18 6<br>0.16<br>§<br>0.14<br><!-- End of picture text -->

<!-- Start of picture text --> 0.12 4<br>0.1 3<br>0.08 2<br>0.06<br>1<br>0.04<br>0.02 7<br>0 “1<br>0 2 4 6 8 10 12 0 2 4 6 8 10 12<br>Quarters since commisioner appointment Quarters since commisioner appointment<br><!-- End of picture text -->
systems, large landowners responded to the threat of land reform by either evicting tenants who could have made claims to land ownership under a possible reform program, or converting them into wage laborers. In the case of eviction, landlords reduced reliance on hired workers either by resuming extensive livestock production and ranching or -aided by significant credit subsidies- by embarking on highly mechanized self-cultivation (Binswanger et al. 1995). This did not only reduce tenant welfare but also depopulated farms and created further difficulties for redistributive land reform. A number of further difficulties of effective land reform in hacienda systems are associated with policy distortions, limitations on the functioning of the land market, and inability to provide the necessary complementary elements for land reform beneficiaries to start successful small farm enterprises.

First, the cost of carrying out land reform were often increased by the continued existence of implicit and explicit policy distortions (e.g. agricultural protection and selective credit subsidies) which drove land prices above the capitalized value of agricultural profits and often disproportionately benefited large producers. Such distortions increased the fiscal cost of land reform policies and reduced their sustainability by making it profitable for land reform beneficiaries to sell their newly acquired land back to large farmers. Indeed, despite attempts to limit beneficiary desertion through imposition of legal restrictions and the threat of punishment in case of contravention, there is considerable anecdotal evidence on land sales by reform beneficiaries in Nicaragua, Colombia, and El Salvador. In a recent Census of Brazilian land reformn settlements, only about 60% of recently established land reform beneficiaries were actually found tilling their land (El Globo 1997).

Secondly, many countries aimed to implement land reform by eliminating or restricting other forms of (rental and sales) market transactions. This completely eliminated price and other market signals, making it more difficult to select beneficiaries and land and further increased the costs of land reform implementation. It also tilted the balance in favor of a highly centralized mode of land reform implementation that has, in a number of countries, led to the domination of land reform processes by formidable (and often corrupt) centralized bureaucracies. In addition, and probably most important, this virtually eliminated beneficiaries' access to credit markets, despite the evidence that without access to such markets, it will be difficult for them to sustain themselves. In some cases, beneficiaries became worse off than they had been before, if a landlord provided them with inputs and possibly even credit for consumption smoothing. In Ireland, for example, a large scale experiment in "negotiated" land reform early in the 2 0th century did not have the expected effect for two reasons. On the one hand it did little to alter the structure of production or to improve tenants' rights. More importantly, however, it actually _worsenedaccess_ to credit, by limiting the ability of 33