rafmacalaba/datause-encoder-data huggingface dataset:
8 _Figure 5: Progressivity of health financing sources in Japan and the Asian Tigers_ * <!-- Start of picture text --> 0.40<br>0.30<br>0.20<br>Taxes<br>0.10<br>Soc. Ins.<br>Priv. Ins.<br>OOP<br>0.00<br>Total<br>-0.10<br>-0.20<br>-0.30<br>Hong Kong, Japan Korea, Rep. Taiwan, China<br>China<br>Kakwani index<br><!-- End of picture text --> The differences in the progressivity of total payments reflect the different shares raised from the various sources (Figure 3) and the progressivity of each source (Figure 5).<sup>†</sup> Taxes are most progressive in Hong Kong (China), which is also the health system that relies most on them in the financing of health care. They are regressive in Japan (largely because of the regressiveness of indirect taxes there<sup>4</sup> ), but progressive—albeit less so than in Kong Hong (China)—in Korea and Taiwan (China).

Social insurance contributions are regressive in all systems where they are used. This is not unusual and typically reflects the effects of contributions ceilings.<sup>13,14</sup> Any exemptions or reduced contribution rates among the poor would reduce the degree of regressiveness, and could in principle make contributions progressive. In Korea<sup>15</sup> and Taiwan (China)<sup>11</sup> there are exemptions or reduced contribution rates for the poor or otherwise disadvantaged (in the case of Taiwan (China) the poorest 1% of households are fully exempt, and a further 4.5% have their contributions paid in full or subsidized). In Japan, contributions emerge as proportional despite the fact that different insurance plans (there are over 5000 plans under the UHI umbrella) have different contribution rates.<sup>16-18</sup> The progressiveness of private insurance—especially so in Taiwan (China)—reflects the fact that it is the better off who buy it. In both Hong Kong (China) and Taiwan (China), private insurance buys supplementary cover. Out-of-pocket payments emerge as proportional in Hong Kong (China) and Korea, mildly regressive in Taiwan (China), and regressive in Japan. The

> * A positive value of Kakwani’s12 index indicates a progressive payment structure, a zero value proportional payments, and a negative value a regressive structure. The data shown are from O’Donnell et al.<sup>4</sup>

> †  The Kakwani indices for total payments are a weighted average of the Kakwani indices for the individual payments sources where the weights are the revenues shares.
# **C Additional results on cross-firm differences in technology**

Appendix C provides additional results on cross-firm differences in technology. In Figure C.1, we examine the CDFs of ABF EXT across countries and provide the KS-based multiple test results on the bottom of sub-figures. The KS-based multiple test, introduced in Goldman and Kaplan (2018), takes its null as follows: where _F_ ( _·_ ) is the first country’s CDF for a value of k and _G_ ( _·_ ) is the second country’s CDF. It tests whether CDFs of two countries are the same for each value of k in the technology measure. Because of the ”multiple testing problem” that make type I error larger than the desired level, the KS-based multiple testing uses a strong ”family wise error rate” (FWER). For example, if FWER is a 5% level, there is no false positives for 95% of the time. We provide the ranges of k when the null is rejected.

To provide a complete picture of our results, we provide the same results for some of omitted technology measures in the main text. Table C.1 provides descriptive statistics for the sample we used in this study. Column (1) presents the overall sample, which is the average of Brazil, Vietnam, and Senegal with the uniform weight. Columns (2) to (4) show the descriptive statistics for each country. Firm-level characteristics include employment, firm age, export, multinational corporation (MNC), and sectors. All estimates are weighted by the sampling weights.

Table C.2 present the average technology for MOST and GAP measures by country as well as relative gap. In Table C.3, we provide regression results using GAP measures as dependent variable. Table C.4 shows the average technology sophistication by sector and country for EXT and GAP measures. Finally, we compute the cross-firm variance of technology sophistication by sector and country for of EXT and GAP measures in Table C.5.

For the regional analysis, we examine the relationship between technology and productivity at the regional-level. As explained above, we compute both region-level technology and productivity based on the FAT data, because the region level GDP per capita (or any other productivity measures) is not available in Senegal and Vietnam. For the region-level technology, we used three measures including the average, cross-firm variance, and average of within-firm variance of technology sophistication. Table C.6 presents the regression results of regional technology sophistication on productivity. Table C.7 shows the relationship between regional cross-firm variance and productivity. Finally, Table C.8 provides the relationship 70
category, the data openness element assigns scores to countries based on the classification of microdata data accessibility summarized in table 4 using the scoring criteria outlined in table 7.

**Table 7: Scoring criteria for the data openness element**

|**Data**<br>**category**|**Data sub-**<br>**category**|**Representative indicator/**<br>**data sets**|**Scoring**criteria|
|---|---|---|---|
|**Firm data**|N/A|Enterprise survey or<br>Annual survey of industry<br>or equivalent.|0 if up to date representative microdata accessibility is<br>classified as “No coverage” or “No openness”.|
|**Price data**|N/A|Commodity price survey<br>or equivalent.|50 if up to date representative microdata accessibility is<br>classified as “Satisfactory openness”.|
|**Individual/**<br>**Household**<br>**data**|**Consumption**<br>**data**|Household budget survey,<br>Household income and<br>expenditure survey,<br>Living Standards<br>Measurement Survey<br>(LSMS) or equivalent.|100 if up to date representative microdata accessibility is<br>classified as “Excellent openness”.|
||**Labor Force**<br>**data**|Labor Force survey or<br>equivalent.||
||**Health data**|Demographic and Health<br>Survey (DHS),<br>Multiple Indicator Cluster<br>Survey (MICS) or<br>equivalent.||
|**Census data**|**Population**|Population census||
||**Economic**|Economic / Business/<br>Establishment census|0 if up to date representative microdata accessibility is<br>classified as “No coverage” or “No openness”.<br>**If available for one or more sectors**<br>25 if up to date representative microdata accessibility is<br>classified as “Satisfactory openness”.<br>50 if up to date representative microdata accessibility is<br>classified as “Excellent openness”.<br>**If available for all sectors**<br>50 if up to date representative microdata accessibility is<br>classified as “Satisfactory openness”.<br>100 if up to date representative microdata accessibility is<br>classified as “Excellent openness”.|

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these drop-out rates are consistent with other similar entrepreneurship training program analyzed in the literature, such as Groh et al. (2012) or de Mel, McKenzie and Woodruff (2012).

Anecdotal evidence from qualitative interviews with graduates, coaches, and professors points to a number of reasons for students dropping out of the entrepreneurship track, including some lack of information and implementation challenges. The program was being implemented for the first time, and in some universities, students were informed late or insufficiently of the modalities of their participation in the entrepreneurship track.<sup>15</sup> The quality of the training and coaching sessions also varied both by training location and by coach or facilitator.<sup>16</sup> Table 3 presents marginal effects from the estimation of a logit model to describe the profile of students who complied with their assignment to the entrepreneurship track and completed it. Students who had a project idea at the time of application were more likely to comply with their assignment to the treatment group by completing the business training and personalized coaching sessions. In contrast, male students were more likely to drop out, and so were students in an Economics, Finance, or Business stream.<sup>17</sup> These students may have been more familiar with the entrepreneurship training content and as such may have perceived a relatively lower value-added of the entrepreneurship track compared to students from other subjects. Variations in drop-out rates across regions are also observed: compared to Tunis, compliance with treatment was lower in Sfax, but higher in Sousse, Monastir, Kairouan, Gafsa, and Jendouba, which are also the regions with relatively higher unemployment rates.

Administrative data reveal high compliance for students assigned to the control group. The twenty days of CEFE training was regularly provided by employment offices so that some control students may also have been able to take the training after graduating, although personalized coaching would not have been available to them. Administrative and survey data show that take-up of the CEFE training was low in the control group, with only twenty-nine students (or 3.4 percent of the control group) completing the CEFE training after graduation.

> 15 A student from Gabes, for example, reported that she was asked by the employment office to decide whether she wanted to remain in the traditional track or take up the entrepreneurship track before she fully understood what the latter entailed. As a result, she eventually decided to drop out of the entrepreneurship track.

> 16 Given the high expectations at the onset of the program, some students were disappointed in the training and dropped out. Other students dropped out due to exams conflicting with the twenty days of training when attendance was mandatory.

> 17 Data from a graduate tracer study in Tunisia suggest that graduates from Economics, Business, and Law were particularly prone to long unemployment spells after graduation (MFPE and World Bank, 2009).

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areas where biomass stock is less than 20 cubic meters per hectare and columns 5 and 6 present results for areas with more biomass.

For overall rural Malawi, higher levels of biomass appear to make an active woman more likely to participate in fuelwood collection but does not affect hours spent on fuelwood collection. Some of the factors such as _Dry Season Dimba Area Interaction_ that are significant for the overall rural Malawi estimation of the active women’s fuelwood collection estimation are only significant for more biomass areas (Columns 1 and 5). Other factors such as _Number of Children below five_ appear to affect overall rural Malawi, and less biomass areas estimations (Columns 1 and 3). Thus, we concentrate on the separate estimates for less biomass and more biomass areas rather than overall rural Malawi.

For both the biomass scarce and sufficient areas, higher stock of biomass increases the likelihood of collection and decreases the hours spent on fuelwood collection. In both cases, factors such as age of the active woman, uncultivated land held by the household and the marriage dummy appear to make an active woman more likely to participate in fuelwood collection. Age squared, wage rate, and substitute for collected fuelwood appear to make an active woman less likely to participate in fuelwood collection in both cases. Our interest is mainly in the number of hours spent in collection of fuelwood. We find that biomass scarcity has a significant but small effect on hours spent in collecting fuelwood. A one cubic meter decrease in biomass per hectare results, on average, in a one minute increase in the time spent by women on collection.

An important question is why there is such a small change in time spent in the face of increasing scarcity. One explanation is that women undertake multiple tasks when they collect fuelwood, which may also be why hours spent in fuelwood do not differ much between the biomass rich north and the biomass poor south. Households also use multiple strategies to cope with biomass scarcity and do not just change their labor allocation behaviors. These conclusions are supported by micro-studies such as that by Brouwer _et al._ (1997), who indicate that women in Ntechu District in central Malawi combine fuelwood collection with field work or change the frequency of collection in response to scarcity. Another explanation for the low variance in labor time in our study may lie in the nature of the data collected, since the labor allocation responses were rounded to half hour segments.

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not seem to increase it. The main reasons are barriers to entry in some sectors, the inefficiency of factor markets, and the focus of the firms’ upgrading program only on some selected sectors.

As for Moussir and Chatri (2019), they examine if Morocco has engaged in a structural transformation process. The analysis of the economic structure reveals a structural inertia, linked to a volatile agriculture sector and poorly and weakly integrated industrial and services sectors. The results of the decomposition show that the intra-sectoral component (within) would account for much of the growth in labor productivity in Morocco.

Our paper makes three main contributions to the literature. First, we provide an overview of the recent empirical literature on structural change. Second, we examine Guinea’s experience of structural change. Though a similar question was analyzed in the World Bank 2018 SCD, our paper uses more recent data, reflecting the GDP rebasing, which Guinean authorities have recently undertaken with IMF support. By focusing on Guinea, we aim at filling the gap highlighted by Herrendorf _et al_ . (2013), who call for more quantitative studies on structural transformation in today’s poor economies. Third, based on existing findings in the literature, we develop a methodology that helps to identify potential drivers of the dynamics of structural change in Guinea. Indeed, though structural change has made a positive contribution to productivity growth in Guinea over the period 2006-15, that contribution has been modest and declining (comparing the period 2006-10 to 2011-15). We develop a methodology that helps to understand factors behind the decline of the contribution of structural change to productivity growth in Guinea.

# **3. Some Stylized Facts on Labor Productivity in Guinea**

For the analysis of labor productivity, we collect our own data from Guinea’s National Institute of Statistics (INS). More specifically, we compute labor productivity as the gross value-added per worker at constant 2010 prices, in millions of GNF.<sup>4</sup> We disaggregate the data into 10 sectors, consistently with the approach developed by Timmer and de Vries (2007, 2009).

> 4 GNF stands for Guinean franc ( _Franc Guinéen_ ), the country’s currency. As of July 5, 2019, one dollar is equivalent to GNF9,160, according to the information from the Central Bank of Guinea, published on: <u>https://www.bcrgguinee.org/marches/taux-de-change.</u> The country uses a managed float exchange rate regime.

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# **Figure 1: Industry Groups, GVCs, and Economic Upgrading**

<!-- Start of picture text --> TYPE OF WORK<br>AGRICULTURE APPAREL AUTOMOTIVE IT HARDWARE BUSINESS SERVICES<br>KNOWLEDGE-INTENSIVE<br>HIGH-SKILLED, TECHNOLOGY-<br>INTENSIVE WORK<br>MODERATE SKILLED, VARIED<br>LABOUR INTENSITIIES WORK<br>LOW-SKILLED, LABOUR-<br>INTENSIVE WORK<br>SMALL SCALE, HOUSEHOLD-<br>BASED WORK<br>ECONOMIC UPGRADING<br><!-- End of picture text --> Adapted from Barrientos et al. (2011).

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The effect of maternal education on child HAZ and stunting is confirmed further in a separate analysis of the relation between HAZ and stunting and maternal education controlling for covariates based on the alternative approaches. Table 3 presents estimates of the relation between years of maternal education and different child anthropometric measures for the sample of all children less than 5 years of age (0-59 months) commonly used in the empirical literature as well as separately for the sample of children less than 36 months and the sample of children 36-59 months of age for which ECD indicators are also available. The estimates obtained using the ad-hoc approach to selecting controls, suggest that maternal education is significantly associated with child linear growth (HAZ), and with a child being stunted or underweight or wasted, irrespective of the age group of children analyzed. In contrast, the DML estimates reveal a much more intriguing picture. Maternal education has a significant effect on the linear growth of younger children (less than 36 months) and no significant effect on the odds ratio of being underweight or wasted among children in this age group. However maternal education has an effect on the odds of being underweight or wasted for the older age group.

Maternal education ceases to have a significant effect on the odds ratio of being stunted among older children (36-59 months) compared to the older children that are not stunted. However, maternal education has an effect on the HAZ of older children, on average, though this effect on linear growth is smaller among older than it is for younger children. For example, an increase in maternal education by one year results in an increase in child HAZ by 0.048 SD of HAZ among younger children, whereas this effect drops down to 0.027 SD of HAZ among older children. In contrast the point estimate of the coefficient of maternal education based on the ad-hoc approach is the same 0.048 SD for both age groups.

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The impact of large-scale migration on poverty, expenditures, and labor market outcomes in Nepal Maheshwor Shrestha<sup>_∗_</sup>

**JEL codes** : F22, F24, J22, I30 **Keywords** : Migration, Poverty, Labor Force Participation, Nepal

> _∗_ The World Bank. Email: `mshrestha1@worldbank.org` . This paper was produced as a background paper to the Systematic Country Diagnostics of Nepal. I would like to thank Ruth Hill for her comments and suggestions, and Sailesh Tiwari for providing me access to the Small Area Estimates of poverty and per-capita consumption for Nepal. The view expressed here do not necessarily represent the views of The World Bank. All errors are my own.
**Figure 20: During discussions in Algeria, the armed groups are demanding a federation for the North. To what extent are you in favor of this proposition? (October 2014) (%)**

<!-- Start of picture text --> 84<br>80<br>62<br>47<br>41<br>33<br>17<br>12<br>7<br>2 2 5 2 4<br>IDPs Refugees Niger Refguees Mauritania Returnees<br>Very much in favor In favor Not in favor Not at all in favor<br><!-- End of picture text --> Source: Listening to Displaced People Survey, 2014.

# **7. Conclusion**

The 2012 crisis in northern Mali led to widespread displacement. The welfare of the displaced – and those who returned– is monitored by combining a baseline survey with structured monthly follow-up interviews carried out by mobile phone. This innovative approach allows tracking changes in welfare with high frequency–even for those who returned to areas that are insecure and inaccessible to enumerators. After 6 rounds of follow-up interviews attrition rates are very low (more than 99% response rate), demonstrating that it is possible to collect robust and representative data from hard-to-reach, conflict-affected populations.

The results show that those who fled were better educated, better off and less affected by violence than the average population in the North. Those who fled lost significant amounts of durable goods (20-60%) and livestock (50-90%); many of their children ended up being taken out of school and their welfare (measured subjectively and by the number of meals consumed) declined considerably. Over time, the impact of the crisis on welfare has lessened and by February 2015 the majority of eligible children of the displaced were going to school and levels of employment and number of meals consumed were at pre-crisis levels. While we have no updated measure of consumer 27
Therefore, the establishment of more assertive and structured guidelines to schools along with close program monitoring would likely improve the program’s implementation and effectiveness (Fundação Itaú Social and World Bank 2015).

One caveat of our report is that we focus only on the sample of schools that joined _Mais Educação_ in 2008 and remained in the program until 2011. It is possible that impact evaluation of different samples of participating schools, for longer and more recent periods, could lead to different results. Another caveat, largely driven by the lack of data, is that our results reference schools’ average impact. It is plausible that the impacts of the program vary within each school according to the characteristics of participating students. It is also possible that, if _Mais Educação_ actually increased student participation on _Prova Brasil_ assessments, the negative impacts could at least in part be driven by this composition effect, especially if the program increased participation of the most vulnerable students. Unfortunately, no data are at hand allowing us to fully test this hypothesis, but the lack of results on the dropout rates suggest this effect is not quantitatively strong. Finally, it is possible that time-varying unobserved variables are correlated with the outcomes of interest (such as changes in directors or teachers’ characteristics), which could affect the estimated results.

# **References**

Almeida, R., Amaral, N., Guedes, P. M., Loureiro, A., and Camila Pinto. 2015. World Bank. Mimeo.

Arzola, M. P. 2010. “Impacto de la Jornada Escolar Completa en el Desempeño de los Alumnos.” Master’s thesis, Instituto de Economía, Pontifícia Universidad Catolica de Chile.

Baker, D. P., R. Fabrega, C. Galindo, and J. Mishook. 2004. “Instructional Time and National Achievement: Cross-National Evidence.” _Prospects_ 34 (3): 311–34.

Bellei, C. 2009. “Does Lengthening the School Day Increase Students’ Academic Achievement? Results from a Natural Experiment in Chile.” _Economics of Education Review_ 28: 629–40.

Berthelon, M. E., and D. I. Kruger. 2010. “Risky Behavior among Youth: Incapacitation Effects of School on Adolescent Motherhood and Crime in Chile.” _Journal of Public Economics_ 95: 41–53.

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prior claims will receive small allotments, the holdings distributed purely for labor will be quite small. People receiving land in recognition of their contribution of labor cannot sell their land for ten years. This is a curious provision, since young people who chose to remain on collective farms are probably the least likely of their cohort to be the universally despised "speculators," who might acquire and sell an asset purely to make some money. The quantity of land tied up by this restriction is not significant.

The Romanian approach to the land distribution appears to have broken through the confusion about how to start the process. Its progress, and that of the land programs in Bulgaria, Hungary, and Poland will be monitored in a study jointly undertaken by The World Bank the member countries.

The fragmentation of very small holdings implicit in the Romanian approach could plague agriculture in the future. Market based solutions to fragmentation of farm land in Western Europe after World War It were not adequate to consolidate holdings, and administrative consolidation was necessary. The chance for success in market based consolidation is greater in Romania now, since all rights are distributed simultaneously and many recipients will be trying to adjust their initial claims before removing the land from collective management. Special programs to promote purchase, sale, and trade over the next year could be highly productive in the longer run. Financing tor land acquisition may be necessary, and subsidized rates for land consolidation would be justifiable. Since the quantities of land traded will be small and the value of land relatively low during the contraction, many buyers will probably choose to pay cash. The cash may come from urban relatives who hold the residual of the monetary overhang. People who put spare money into land are more likely to be relatives than the feared land speculators.