rafmacalaba/datause-encoder-data huggingface dataset:
characteristics, as in Table 8, and then obtain predictions from the model by setting all expert characteristics at their ideal (i.e., most “expert”) level. Many of the variables in the models in Table 8 have ideal levels, such as having a Ph.D. instead of an M.Sc. or a B.Sc. and having considerable experience doing SP studies (many surveys, many publications). Other variables, such as gender, do not, and can be set equal to the mean across the experts from a given country.

# **3.3 Effects of Other Variables on Experts’ WTP Predictions**

The models in the first pair of columns in Table 8 imply that the only expert characteristics that are significantly (5%) correlated with their WTP predictions are the number of SP papers they had reviewed (a negative effect) and the B.Sc. dummy (a positive effect). Estimates that are less prone to omitted variables bias resulting from correlation of expert characteristics with unobserved country characteristics can be obtained by estimating models that include country fixed effects. Results for these models are shown in the last two columns of Table 8. Inclusion of the fixed effects knocks out the income variable and the regional dummies, but it reveals that several additional expert characteristics have a significant effect on the WTP predictions.

We find that reading the four most established journals (PC1) has a positive effect, as does publishing a larger number of SP papers and having an M.Sc. or (especially) a B.Sc. instead of a Ph.D. While the association with highest degree likely represents a learning effect, the association with journal reading might represent either that, a selection effect, or more likely a combination of the two. Interestingly, the number of CV surveys undertaken has a negative effect, as does the number of CE surveys undertaken (although it is less significant). This could indicate a desirable learning effect. When combined with the positive effect of the number of SP papers published, it suggests that experience with publishing and experience with surveys are qualitatively different dimensions of expertise.

Experts who rated conducting a CV study like the one described in the Delphi exercise as more difficult had lower WTP predictions. Other expert characteristics were generally not found to have significant effects.

estimates and this characteristic is at the same time negatively correlated with income in experts’ home countries. Such a correlation can distort the estimated relationship between country income and WTP.

22
Policy Research Working Paper 8147

# **Abstract**

This paper discusses the challenges associated with collecting time-use data in developing countries. The paper suggests potential solutions, concentrating on the two most common time-use methods used in development settings: stylized questions and time diaries. The paper identifies a significant lack of rigorous empirical research comparing these methods in development settings, and begins to fill this gap by analyzing data from Women’s Empowerment in Agriculture Index surveys in Bangladesh and Uganda. The surveys include stylized questions and time diary estimates for the same individual. The study finds limited evidence that stylized questions are more feasible (in terms of interview length) but also less accurate, compared with time diaries. These results are attributed to the relatively greater cognitive burden imposed on respondents by stylized questions. The paper discusses the importance of broadening the scope of time-use research to capture the quantity and quality of time, to achieve richer insights into gendered time-use patterns and trends. The paper suggests a path forward that combines mainstream time-use data collection methods with promising methodological innovations from other disciplines.

This paper is a product of the Gender Innovation Lab, Office of the Chief Economist, Africa Region and the the Living Standards Measurement Study, Development Data Group. 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://econ.worldbank.org. The authors may be contacted at G.Seymour@cgiar.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
<!-- Start of picture text --> Number of episodes<br>150 == Wars Natural disasters<br>= Financial crises<br>100<br>50<br>0<br>1960-79 1980-99 2000-18 1960-2018<br><!-- End of picture text --> <!-- Start of picture text --> Number of episodes TM 1960-1979<br>120 = 1980-1999<br>100 = 2000-2018<br>80<br>60<br>40<br>20<br>0<br>Natural disasters Wars Financial crises<br><!-- End of picture text --> <!-- Start of picture text --> Number of episodes = 1960-1979<br>= 1980-1999<br>30<br>= 2000-2018<br>20<br>10<br>0<br>Natural disasters Wars Financial crises<br><!-- End of picture text --> <!-- Start of picture text --> Number of episodes = Natural disasters<br>5 = Wars<br>4<br>3<br>2<br>1<br>0<br>1960-79 1980-99 2000-18 1960-2018<br><!-- End of picture text --> <!-- Start of picture text --> Number of episodes TM 1960-1979<br>25 TM 1980-1999<br>= 2000-2018<br>20<br>15<br>10<br>5<br>0<br>Natural disasters Wars Financial crises<br><!-- End of picture text --> <!-- Start of picture text --> Years mMean Median<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Natural disasters Financial crises Wars<br><!-- End of picture text -->
Table 3: Alternative approaches: Sample or data restrictions

|_Sample/Data_|Inf. in|terval|100 en|dpoint|First swi|tch only|No|MSB|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|WTP|6.366<sup>_∗∗∗_</sup>|-1.551|6.086<sup>_∗∗∗_</sup>|-1.459|1.824|3.695|8.732<sup>_∗∗∗_</sup>|-3.943|
||(2.45)|(2.84)|(2.31)|(2.62)|(2.73)|(3.11)|(2.87)|(3.96)|
|N(subjects)|266|228|271|235|271|228|223|166|

Notes: Interval regression estimates, using the first and last switch to define the interval in the case of MSB. Columns 1 and 2 allow an infinite interval for NSB. Columns 3 and 4 impose an endpoint of _±_ 100 on the open interval for NSB observations. Columns 5 and 6 use the first observed switch only. Columns 7 and 8 drop subjects who exhibit MSB. In odd-numbered columns, the value of the voucher is descending; in even-numbered columns, it is ascending.

WTP estimation. In particular, the single-point approaches yield WTP estimates that are so sensitive to the choice of point within the interval to be more or less useless. But even the interval regression estimates deviate from the probit estimates. Moreover, in all cases order effects reverse the sign and alter the significance level of the WTP estimate, emphasizing the need for order randomization.

We note one exception regarding using scaled probit over interval regression, and that is when subjects do not exhibit MSB. In this case, which may be more common in highincome settings or in the laboratory, interval regression or simple probit is the correct approach due to the identification problems in random-effects probit when there are no observed choice errors (see above).

# **4 MPL implementation**

In the course of our own work and while surveying the literature, we identified a number of other design and implementation features besides order randomization that can help to decrease bias and individual error. We discuss some of these design features below. The SurveyCTO template that we provide as part of this paper, described in Appendix S2, implements these features along with a very flexible specification for order randomization. Many of these features may also be beneficial in other implementations of MPL, such as pen-and-paper questionnaires or laboratory experiments.

31
Table 3b (continued)

||Share<br>industry|of employ<br>who are|ees in<br>women|Share of<br>female-o|female emp<br>wned establ|loyees in<br>ishments|Share of<br>male-ow|male emp<br>ned establ|loyees in<br>ishments|
|---|---|---|---|---|---|---|---|---|---|
||2001|2006|2010|2001|2006|2010|2001|2006|2010|
|**55** Hotels and restaurants|0.182|0.192|0.166|0.656|0.657|0.765|0.864|0.853|0.884|
|**60** Land transport; pipelines|0.009|0.011|0.004|0.190|0.141|0.478|0.992|0.991|0.997|
|**61** Water transport|0.015|0.002|0.011|0.500|1.000||0.985|1.000|0.989|
|**63** Supporting/auxiliary transport activities; travel agencies|0.047|0.049|0.093|0.451|0.291|0.609|0.971|0.962|0.916|
|**64** Post and telecommunications|0.142|0.148|0.162|0.497|0.768|0.905|0.919|0.915|0.932|
|**65** Financial intermediation|.|0.260|0.373|.|0.844|0.963|.|0.877|0.946|
|**66** Insurance and pension funding, except social security|.|0.068|0.082|.|0.636|0.970|.|0.990|0.984|
|**67** Activities auxiliary to financial intermediation|.|0.141|0.103|.|0.788|0.872|.|0.957|0.971|
|**70** Real estate activities|0.049|0.124|0.098|0.341|0.856|0.946|0.963|0.954|0.969|
|**71** Renting machinery/equipment & personal/household goods|0.037|0.038|0.035|0.444|0.563|0.658|0.973|0.975|0.982|
|**72** Computers and related activities (for business)|0.135|0.135|0.090|0.521|0.670|0.700|0.914|0.901|0.932|
|**73** Research and development|0.142|0.000|0.000|n.a.|n.a.|n.a.|0.858|1.000|1.000|
|**74** Other business activities|0.084|0.101|0.131|0.549|0.597|0.823|0.941|0.921|0.909|
|**80** Education|0.389|0.414|0.424|0.868|0.844|0.847|0.744|0.720|0.684|
|**85** Health and social work|0.186|0.239|0.233|0.828|0.856|0.833|0.896|0.854|0.834|
|**90** Sewage, refuse disposal, sanitation, and similar activities|0.841|0.509|0.221|0.996|0.909|0.968|0.567|0.745|0.962|
|**91** Activities of membership organizations n.e.c.|0.024|0.028|0.009|0.618|0.614|0.792|0.997|0.977|0.994|
|**92** Recreational, cultural and sporting activities|0.081|0.051|0.045|0.617|0.605|0.542|0.970|0.963|0.973|
|**93** Other service activities|0.268|0.272|0.230|0.797|0.868|0.929|0.781|0.814|0.895|
|Nationwide|0.162|0.168|0.156|0.738|0.778|0.850|0.890|0.892|0.907|

Notes: See Table 1.
of Figure 3. The first panel shows the evolution of the Chinese GDP growth rate after a one percent decline. Possibly due to the feedback effect, a one percent decline in the GDP growth rate results in 1.6% reduction of the real GDP in levels after 20 quarters for China. For the United States, the Eurozone, and Japan, the impact of a negative shock on Chinese GDP is increasingly negative as we use more recent trade weights. However, the GIRFs have a negative shape when using the trade weight matrices of 1985 and 1995, and these lines are near zero. Therefore, it may imply that a negative Chinese shock would have had minimal or nonexistent effect on these economies in 1985 and 1995.

In terms of the magnitude, we notice that the US experiences the most pronounced impact compared to the Eurozone and Japan both in the short-term as well as the long-term. In the long-term, the size of US GDP drops by 0.07%. On the other hand, for the Eurozone and Japan, the size drops by approximately 0.05%. In terms of scale, the recent impact is approximately 12 times bigger than that of 1985 for the US and the Eurozone. For Japan, it is about 20 times bigger. Thus, we conclude that, over three decades, the US and Eurozone exhibit similar responses both qualitatively and quantitatively. We will test the significance of the negative impact in the next section.

The second to fourth rows of Figure 3 show the GIRFs for a one percent decline in China’s growth on our set of Asian countries. As in the developed countries’ case, the impact of a negative shock has become progressively negative on the Asian economies using more recent trade weight structures. The Republic of Korea, Malaysia and Singapore had a nearly nonexistent impact with 1985 trade weight. Similar phenomena, but a slightly negative impact, are observed for other countries.

Under more recent trade structures, every country except the Philippines experiences a negative shock either in the short term or in the long term; however, the level and extent of the shocks differ greatly. Indonesia is by far the most negatively impacted country both in the short term as well as the long term. It is followed by Thailand (-0.095% in the long term), the Republic of Korea (-0.070%), Singapore (-0.050%), Malaysia (-0.050%), and 25
# **1 Introduction**

Latin American countries achieved important social progress in the 2000s. Poverty fell by over 16 percentage points, from 39.5 to 23.3 percent of the population between 2004-14; while extreme poverty was more than halved (declining from 24.5 to 10.8 percent) over the same period<sup>1</sup> . The middle class in the region expanded<sup>2</sup> . And income inequality, a persistent malady in Latin America, fell substantially during the 2000s (the regional Gini coefficient for per capita income declined from 0.57 to 0.51 in 2000-14) (World Bank, 2016). The role of rising labor income stands out, among other factors contributing to these achievements, as a key channel of transmission between growth and social gains. Indeed, income from labor has been a central element in the reduction of poverty. For instance, Rodriguez-Castelan et al. (2016) establish labor income as the leading driver behind the reduction in poverty between 2003 and 2013 in the region. What happens in terms of labor market dynamics is thus fundamental to understanding wellbeing in Latin America and the Caribbean, particularly if the interest is on the poorest segment of the population. As documented by Cord et al. (2014), data from household surveys across the region show that incomes from labor make up 60 to 80 percent of total household income.

– The study of job quality including characteristics such as whether jobs are – stable and pay, at the very least, a living wage is useful to shed further light on 1Based on poverty lines of $4 a day and $2.50 for poverty and extreme poverty, respectively (World Bank, 2016).

> 2Growing from 23 percent of the population in 2003 to 35 percent of the population in 2014 (World Bank, 2016). Following Lopez-Calva and Ortiz-Juarez (2011), and Ferreira et al. (2013), the middle class is defined as the population with a consumption per capita of US$10-US$50 per day in PPP 2005 (which distinguishes this group from the vulnerable, i.e. those with a consumption per capita between $4 and $10 per day).
# _4.5 Does the Effect Persist in the Eighth Grade?_

Estimates presented in section 4.3 shows weak evidence that older students are less adversely affected by classroom age variance than younger students in the fourth grade. However, does classroom age variance continue to impede achievement as students enter higher grade levels? Since the cohort of fourth graders in TIMSS 2003 attended grade eight in 2007, it is possible to examine whether eighth-grade classroom age variance continues to affect their achievement. However, because individuals were not followed over time in TIMSS, I can only compare the performance of the same grade cohort in countries sampled in both waves of TIMSS, leaving us a sample from three countries.

There are a number of limitations when the same grade cohort is compared in the two waves of TIMSS. First, the same set of students was not followed over time and sampling differences across the two waves of TIMSS make comparison less reliable. Second, in addition to sampling variation, individuals most negatively affected by classroom age heterogeneity might repeat grade or drop out of school, and hence not observed in the eighth grade sample, leading to potential selection bias against the finding of a negative effect. Third, students who attended ability mixing schools in the fourth grade may switch to ability grouping schools in the eighth grade, making the comparison less meaningful. Fourth, the structure of eighth grade courses may also introduce error in the measure of classroom age heterogeneity, as eighth graders are more likely to take different courses that vary in difficulties with different set of peers. Despite these shortcomings, comparing the effects of classroom age variance on achievement for the same cohort of students within ability-mixing schools is the only option to gauge whether classroom age variance persists to impede achievement as students advance to higher grade levels.

20
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# **1 Introduction**

Changes in economic policy, especially those concerning subsidies of staple goods and utilities, are often controversial. There is often a negativity bias in public perceptions towards changes in policy arising from resistance to a change in the status quo or a lack of understanding of the effects of these changes, which may even arise among those who stand to gain [Fernandez and Rodrik, 1991]. Calvo et al. [2014] recently investigated the public perceptions of a specific program of gas subsidy reform which was implemented in April 2011 in El Salvador. Household survey data were used to illuminate the dimensions underlying this perception such as political partisanship, level of information about the reform, and trust in the government’s ability to deliver the subsidy after the reform. Survey data were also analyzed from the period following the reform’s implementation showing how the role of these different factors evolved.

The reform considered for this work is an example of a reform that was initially unpopular despite the fact that the majority of the population stood to gain from it [Tornarolli and Vazquez, 2012]. The reform involved changing the subsidy from producers to consumers. Instead of subsidizing prices at the point of sale, the new mechanism delivered an income transfer to a large set of eligible households. As a result of this change the consumer price increased from $5.10 (the subsidized price) to $13.60 (the price without subsidy). Individual households received a transfer $8.50 per month provided they were eligible. The eligibility requirement was consuming less than 200 Kwh in electricity per month, a criterion that was meant to exclude the highest income brackets of the population from receiving the gas subsidy. Households that lacked electricity needed to register at a governmental office and provide their address so that the household received a card ( _tarjeta_ ) that entitled it to collect monthly the $8.50.

The evolution of sentiment regarding the reform was investigated using household surveys conducted by _La Prensa Grafica_ , the largest newspaper in El Salvador. The surveys were conducted in 6 different time periods and covered demographic questions such as income and political views. It was demonstrated that the overall sentiment towards the reform could be effectively accounted for by considering both the individual’s perception of the government’s ability to enact the reform and political affiliation.

In recent years social media has emerged as a novel and promising alternative means to extract societal level information. These data are useful for a variety of purposes including measuring brand perception, stock trading [Bollen et al., 2011] and civic participation [Bond et al., 2012]. More recently, such data sources and appropriate analysis techniques have been co-opted in order to improve the wellbeing of vulnerable popu2
**Table 5 Job Creation and Job Destruction: Lithuania Against Selected Countries**

> **(as percentage of total employment)**

||Tra|nsitioneco|nomies||OE|CDecono|mies|
|---|---|---|---|---|---|---|---|
||||||||**United**|
||**Lithuania**<br>1998-99|**Poland**<br>1998-99|**Russia**<br>1998-99|**Slovakia**<br>1997-98|**France**<br> 1984-91|**Germany**<br>1983-90|States<br>1984-91|
|Jobcreationrate|13.6|9.7|12.2|2.0|12.7|9.0|13.0|
|Openings|3.8|4.4|||6.1|2.5|8.4|
|Expansions|9.7|5.3|||6.6|6.5|4.6|
|Jobdestructionrate|17.7|11.5|13.8|6.9|11.8|7.5|10.4|
|Closures|7.0|1.4|||_5.5_|1.9|7.3|
|Contractions|10.7|10.1|||6.3|5.6|3.1|
|**Net employmentchange**|-4.1|-1.8|-1.6|-4.9|0.9|_1.5_|2.6|
|Continuingestablishmentsonly|-0.9|-4.8|||0.3|0.9|_1.5_|
|Jobturnover rate|31.2|21.2|26.0|8.9|22.4|16.5|23.4|
|Continuing establishmentsonly|20.4|15.4|||12.9|12.1|7.7|
|Excessjob**reallocationrate**|27.1|19.4|24.4|4.0|21.5|15.0|20.8|
|Continuingestablishmentsonly|19.4|10.5|||12.6|11.2|6.2|

> Note: data for OECD countries are yearly averages, data for transition economies refer only to one year.

> _Sources: Lithuania: Annual survey of wages and salaries (DA-03), vanousyears, Bank staff calculations._

_Poland: World Bank (2001b)_ _Russia: Broadman and Recanatini (2001) Slovalaa. WorldBank(200Ic) OECD countries OECD (1996)_ All these clearly indicate that Lithuania has been undergoing a process of fast and very intensive enterprise restructuring, which is hardly paralleled by any transition or mature market economy. This process has been associated with a considerable job turnover, far exceeding that observed in most other countries. Each year there is a large number of new jobs being created, and simultaneously, an even larger number of old jobs is being destroyed. This implies a decline in the overall number of jobs, but also farreaching reallocation of jobs from less to, presumably, more productive uses. This process of intensive and fast job reallocation indicates a dynamic labor market, with a 13