rafmacalaba/datause-encoder-data huggingface dataset:
At endline, we also interviewed students from Grades 4 and 5, in order to characterize actual use of textbooks; the books distributed by the program were targeted to Grades 4 and 5.

We were unable to gather data on 5 percent of the schools originally designated to treatment and control groups. Table A1 shows the correlates of attrition at endline. The dependent variable is a dummy equal to 1 if the school attrited by the time of the endline survey, and we include a number of control variables. We estimate the attrition regressions using a linear probability (ordinary least squares, or OLS) model. We estimate the relationship between attrition and both assignment to treatment (column 1) and actual treatment (column 2); results are consistent across both estimates. In both specifications standard errors are clustered at the school level. Table A1 shows that neither being assigned as a treatment school nor actually receiving additional books predicted attrition. This is robust to controlling for a number of baseline household characteristics: number of classrooms, electricity, distance to the DEO’s office, and number of students. From both models we see that having a greater number of classrooms at baseline decreases the probability of attrition, while having electricity increases it, although none of the other controls are significant predictors (except the dummy variable for having missing data on the distance to the DEO’s office). Overall, these balanced rates of attrition across treatment and control groups suggest that the impact evaluation results are unlikely to be driven by attrition.

# **4. Education Service Delivery in Sierra Leone: Evidence from the Baseline Survey**

One issue that emerged from the baseline survey in the surveyed councils (Kailahun, Kambia, Pujehun, Western Urban and Western Rural) is the stark supply-side disadvantage that remote areas face with respect to education service delivery. Pujehun, which is one of the more remote councils in Sierra Leone, was significantly worse off compared to the other regions in terms of the supply of schooling inputs. In this region, less than 50 percent of all schools were considered to be in good condition, with almost 20 percent falling under the category “no roof, walls are heavily damaged, needs complete rehabilitation.” Further, almost one-third of the schools did not have a working toilet.

During the baseline survey (May 2008), unannounced classroom visits were carried out in Grade 4 and 5 classrooms. Out of 828 such classrooms, teachers were found to be actively teaching in 54 percent of cases. If we include classroom management activities 8
Tables 9-13 report the results of the regressions of trainer characteristics on student psychology scores measured post training, among the sample of entrepreneurs who participated in the trainings. The regressions use ANCOVA estimation with standard errors clustered at the classroom level (the six TVET colleges had multiple training rounds where the student could have attended a training) and include the baseline measure of the dependent variable, i.e., the lagged psychology score is controlled for.

Column (5) in each table shows that there is a statistically significant positive association between students' psychology and the trainer having ever been a business owner themselves even after sequentially adding different sets of controls. Personal initiative, self-efficacy, locus of control and error competence measured 1.5 years after the training are all significantly higher among the students who had a trainer who had ever owned a business. These associations are stronger when the years as a teacher and years at TVET colleges are controlled for. The women entrepreneurs appear to benefit from having a shared identity with their trainer. The TVET trainers’ limited exposure to the world of entrepreneurship may be one of the major barriers to successfully train women entrepreneurs on these socioemotional skills.

A supplemental qualitative analysis using videos of five trainers offering PI training sheds some light on how differences between trainers’ attributes may manifest during the training (Wolf & Frese, 2019). The systematic comparison of training videos suggests that the PI trainer who has owned a business before differs from the remaining PI trainers with regard to his teaching behavior. First, according to the evaluation of three independent Ethiopian raters, the owner trainer is unanimously perceived as more competent, confident, and enthusiastic than his four non-owner trainer colleagues. Second, a qualitative content analysis of training video transcripts reveals that he has a more profound understanding of the training content than the non-owner trainers. They are more likely to communicate learning intentions, to make meaningful connections to students’ daily life, and to provide informative feedback than his colleagues. It is conceivable, for example, that due to their own entrepreneurial past, the trainer finds it easier to relate the training content to students’ daily experiences and challenges as entrepreneurs. Such teaching behaviors,
22 substitute present consumption for future one, as the cost of the latter is larger in the context of a cash in advance model. In contrast, when there are expectations of a slower pace of disinflation than the announced one, it will lead to a substitution of consumption from the present to the future, thus creating a recessionary tendency at the early stage.

# _4.2. Denmark vs. France and Ireland_

In Denmark, disinflation was accompanied by an average GDP growth of 3.7 per cent over the years 1983 to 1986. Instead, in France and Ireland the reduction of inflation to levels compatible wit those of other OECD countries brought about severe reductions in output growth and employment. What explains this difference?.

Expectations seem to have played an important role. This role was not similar to that played in Latin America. There, the issue was whether the stabilization would be long lasting or not. This question mark was not present in Europe. Instead, agents' expectations operated through two different channels, explaining the striking differences in the behavior of real variables: The first channel was an _"over-confidence"_ effect observed in Denmark. The mere announcement of the disinflation program by the government triggered very optimistic hopes for future activity. This expectations operated on the demand side, and explain the increase in consumption expenditures and the spectacular behavior of investment. These optimistic expectations are consistently revealed for the case of Denmark by various indicators of future economic activity"<sup>6</sup> .

Figure 8 shows a comparison of the Index of General Economic Situation and real GDP. The Index of General Economic Situation (GES) provides a forecast of the following year's state of the economy." IG This section uses different indicators of consumer confidence published by European Economy Supplement B. See Appendix 1 for details.

17The series shown in figures 8 to 10, and 12 have been standardized so that they all have a comparable variation. The rescaling procedure used was to subtract the mean and divide by the standard deviation.
We do not have panel data spanning 1993-2004.<sup>26</sup> Thus we cannot measure welfare changes over time for those who were farmers in 1993 but landless in 2004. Nor can we trace farmers who became landless and moved to urban areas (which is also impossible with most panels). Observed changes over time will reflect (in part) the changing internal composition of given socio-economic groups. For example, if relatively worse-off farmers sell their land this will put upward pressure on the poverty rate among the landless. Comparing a poverty measure for the landless over time, we cannot say how much is due to changing living conditions among the initially landless versus low living standards among those who become landless.

We do not have data on wealth, and we use consumption expenditure instead (the same consumption measure used in measuring poverty and inequality). We use a measure of household consumption per capita that is comparable across the four surveys, is expressed in real January 1998 prices and is appropriately deflated for spatial and monthly price variation.<sup>27</sup> Consumption includes the imputed rental value of housing and the use value of durable goods (see World Bank 1995, 2000; Glewwe, 2003, 2005). Figure 2 gives the frequency distribution of log consumption per person for 1993 and 2004 (these will help interpret later results); the upper panel is for rural Vietnam as a whole. (The lower panel is for the landless, which we return to.) We have no choice but to use current consumption, which will reflect impacts of the land reforms after 1993. (Ideally we would have pre-intervention consumption but this would require panel data.) We will comment on likely biases arising from this feature of the data.

The land concept we use is the amount of “cultivated land” in the annual, perennial, water-surface and forest categories. We define a household as landless if it has no land other

> 26 The first two surveys contain a subset of the sample that form a panel, as do the 2002 and 2004 surveys; but there is no panel link between the 1998 and 2002 surveys. There is also likely to be selective attrition in the two panels, in particular by those who sold/lost their land.

> 27 In designing the 2002 and 2004 surveys, considerable effort was made to ensure that household consumption could be measured in a comparable way to previous surveys.

18
shared by different characters and asked to indicate whether they believed the messages were spreading misinformation or not. In the comprehensive version of the game, messages included examples using misinformation spreading techniques and examples of messages that did _not_ use misinformation techniques.

After each question, participants would get feedback about whether their responses were correct or incorrect, and the chatbot leveraged several gamification strategies. For example, participants received visual feedback about their scores that persisted throughout each round (e.g., stars for correct responses and blank squares for each incorrect response). In addition, participants who correctly identified the message spreading misinformation in a round received a virtual ‘badge’. At the end of the game, participants received a recap of their performance and the various badges they earned. See **Appendix** **_A_** for screenshots from the game.

# **_Brief Active Inoculation_**

The brief active inoculation condition was the same as the comprehensive inoculation game except participants were exposed to only examples of messages that used misinformation techniques. To shorten the game, all neutral examples were excluded from the training rounds. **_Passive Inoculation_** Participants assigned to passive inoculation were sent three infographics adapted from the UNESCO resources on media and information literacy (UNESCO, 2022) with imagery and text referencing COVID-19 removed. The three infographics (see **Appendix** **_B_** ) covered information that paralleled the lessons taught in the three rounds of gamified training. Specifically, they addressed: (1) that disinformation was often designed to trigger emotional reactions, (2) that false experts are often used to spread manipulative information, and (3) how to spot false content and rumors. The infographics were described to users as tips that would help them perform well 12
Table 8. Effect of a 1-Percentage Point Increase in the Income Share Held by the 3<sup>rd</sup> Quintile on Investment-to-GDP Ratio

||Level of Economic Development:<br>1970 GDP per capita|Level of Economic Development:<br>2010 GDP per capita|
|---|---|---|
||(1)|(2)|
|Brunei|0.11|0.17|
|Cambodia|-0.05|0.05|
|Indonesia|-0.06|0.07|
|Laos|-0.07|0.05|
|Malaysia|-0.02|0.11|
|Philippines|-0.02|0.06|
|Singapore|0.03|0.17|
|Thailand|-0.02|0.10|
|Vietnam|-0.07|0.06|
|ASEAN Average|-0.02|0.09|
|ASEAN Median|-0.02|0.11|

Note: The table reports effects on the investment-to-GDP ratio of a 1 percentage point increase in the income share held by the 3<sup>rd</sup> quintile. For column (1) the predicted effects are generated by combining estimates reported in Brueckner and Lederman (2015) with the values of countries' GDP per capita in 1970. For column (2) the predicted effects are generated by combining estimates reported in Brueckner and Lederman (2015) with the values of countries' GDP per capita in 2010. Specifically, the estimated relationship for country _c_ and period _t_ is INV/GDPct = -26*Q3ct +4*Q3ct*ln(initial GDP per capita)c.

22
8 Figure 3: Catastrophic spending gap <!-- Start of picture text --> Out-of-pocket payments<br>as % income<br>Catastrophic payment gap<br>threshold<br>Cumul. % pop. ranked in decreasing<br>% exceeding threshold<br>order of out-of-pocket payments<br>as % income<br><!-- End of picture text --> Source: Wagstaff and van Doorslaer<sup>3</sup> .

A final modification is to make some allowance for whether it is well-off households who exceed the threshold or worse-off ones. It seems likely that policymakers would be more concerned if it is the latter rather than the former. One could tabulate the incidence of catastrophic payments and the catastrophic payment gap by pre-payment income quintile, or one could compute a concentration index for each.<sup>3</sup> The concentration index for the catastrophic health expenditure ‘headcount’, for example, would be negative if catastrophic expenditures were, on average, more common among the worse off. Of course, it could be that that the fraction of the population experiencing catastrophic spending has increased over time, but has become less concentrated among the poor. A natural summary measure that takes both into account is the catastrophic payment headcount multiplied by the complement of the concentration index.<sup>3</sup> This is equivalent to constructing a rank-weighted average of the catastrophic payment indicator
episodes of trade liberalization. This increase in prices and quantities cannot be solely explained by the increased (product-level) import competition from developed countries.

Most of the empirical work looking at the determinants of skill upgrading in developing countries has traditionally focused on aggregate sector data and/or on the role of foreign direct investment. For example, Harrison and Hanson (1999) find evidence for Mexico consistent with FDI acting as a channel for the spread of technology across countries. This literature is based on the assumption that foreign firms bring to their overseas subsidiaries a variety of managerial, organizational and technical innovations that otherwise would not have available to the host country. Skilled labor is needed to adapt and diffuse these innovations further in the host economy. Increased firm openness could also lead to a larger technology diffusion, which in turn could affect the demand for skills. In particular, firms importing state of the art intermediate inputs would be naturally exposed to greater technology diffusion. Similarly, firms present in international markets are also more pressured to innovate by international competition. Therefore, both activities may require higher ratios of skilled labor. Alternatively, greater openness could lead to a greater specialization in the production of goods intensive in unskilled labor (factor relatively abundant in developing countries). Therefore, a lack of skill upgrading, or even skill “downgrading” could be suggestive of openness leading to a relative specialization in low skill intensity products.

The evidence for developing countries on these links is quite scarce. For Latin America, Fajnzylber and Fernandes (2004) explore cross-sectional firm level data for Brazil. They find that increased levels of international integration (including foreign ownership) were associated with an increased demand for skilled labor. However, they find the opposite for China where integration was negatively associated with the use of skilled labor. Consistent with this positive association between skilled labor and firm openness, Almeida (2008) shows that in East Asia more globally integrated firms and those adopting newer technology are more likely to take longer to fill out external job vacancies, especially for the skilled positions in middle-income countries. This is interpreted as evidence that technology, especially that transferred from abroad, has been biased towards more skilled labor and that the supply of skills has not been adjusting fast enough. As micro data sets become available with longitudinal information, economists 3
**Getting the (Gender-Disaggregated) Lay of the Land: Impact of Survey Respondent Selection on Measuring Land Ownership and Rights**

Talip Kilic<sup>‡</sup> , Heather Moylan*, and Gayatri Koolwal<sup>§1</sup>

**JEL Codes:** C81, C83, J16.

**Keywords:** Gender, Land, Respondent Selection, Household Surveys, Malawi, Sub-Saharan Africa.

> 1 ‡Senior Economist, Living Standards Measurement Study (LSMS), Data Production and Methods Unit, Development Data Group (DECDG), The World Bank, Washington, DC; tkilic@worldbank.org.<sup>*</sup> Survey Specialist, LSMS, Data Production and Methods Unit, DECDG, The World Bank, Rome, Italy; <u>hmoylan@worldbank.org.</u><sup>§</sup> Consultant, LSMS, Data Production and Methods Unit, DECDG, The World Bank; <u>koolwalg@devscience.org. The authors thank</u> Kathleen Beegle for her comments on the earlier draft of this paper.
The CONALEP (1994) and (1999) tracer studies had several problems, one of the most important being the lack of a well defined control group. A control group was expected to be added later, using data from the National Urban Employment Survey (ENEU). However, the studies neither include in-depth information on how the analysis was performed nor do they provide useful information on how CONALEP graduates perform relative to a control group.

Lane and Tan (1996) also encountered several problems in their evaluation. The first was the construction of a non-arbitrary control group. The ENEU sample is representative of metropolitan areas while the CONALEP graduate tracer survey is representative nationally. The difference in geographical coverage of the two groups makes comparison difficult. Second, the control groups were constructed ad hoc. The control groups included individuals between the ages of 17 and 30: (a) those who have completed lower-secondary education; (b) those who have completed non-professional, elementary vocational training (CECATI), and (c) those who have completed one to three years of general academic (non-vocational) high school. Some doubts remain with respect to the second group, since the ENEU survey does not distinguish between formal and informal training/technical courses.

Lee (1998) compares the individuals from the _Encuesta de Egresados_ 1994 (the treatment group) with two other groups. One group comprises all 1991 graduates from upper-secondary diversified technical education programs; this group's labor force participation and employment performance in January 1994 was compared with that of CONALEP graduates of 1991, and of 1991-93 combined. The first comparison group was created from a mail survey of all graduates, with a 45 percent response rate, and therefore is likely to be biased toward those who were either employed, studying, or had a higher level of earnings. The second comparison group was made 19
# **5. Results**

## **5.1. Main estimation**

The estimates are based on our main sample which includes households with children aged 1–6 in Uzbekistan from 2018 to 2022. Our main specification includes individual and household covariates as well as time and district-fixed effects. The first two columns of Table 2 present an estimation of household-level analyses where the dependent variable is the number of women in the labor force and working women in the household. The rest of the columns show individual-level analyses among all working-age women living in those households. In the last four columns, we further separated mothers from the rest of the working-age women and estimated the effect of the policy reform separately. As we mentioned in Section 3, the policy is measured in 2 different ways: the annual coverage rate in each district relative to 2018 and further by comparing employment outcomes of women living in fast-implementing districts after the reform to the same group of women living in slow implementing districts.

We find a positive and statistically significant effect of the public childcare expansion on women’s labor force participation and employment rates. Specifically, our estimates suggest that a 10 percent increase in childcare coverage leads to a 1.38 percentage point increase in the probability of labor force participation and a 0.63 percentage point increase in the probability of employment for women. In districts where the coverage rate rose above the median (more than 17 percent), the policy increased female labor force participation by 2.4 percentage points and the employment rate by 1.7 percentage points. Additionally, after excluding 7 districts and the capital city (approximately 10% of the total sample) and reanalyzing the data (as explained in the section 4), we obtained similar results, which are presented in Appendix Table 3.

Although a 2.4 percentage point increase may seem modest, it is quite substantial when considered in the context of the low baseline labor force participation rate for women, which averages just 20 percent. This means the policy effect translates to a 12 percent increase in female labor force participation, a noticeable improvement given the entrenched social barriers that limit women’s access to the workforce. The magnitude of this increase indicates that public childcare expansion has a meaningful impact in encouraging more women to participate in the labor market.

Additionally, the findings show that this employment effect is shared almost equally between mothers and other women, as the estimated coefficients for both groups are relatively similar. However, while the policy successfully increased labor force 13