rafmacalaba/data-use-mentions-extended huggingface dataset:
lnyijt = β0 + β1 × lnFDIijt + γit + θjt + μij+εijt, (1) where the yijt refers to the labor productivity of country _i_ sector _j_ in year _t_ , FDI ijt the amount of FDI at the same level and the error term obeying the standard normal distribution. To control for the country-level factors such as the institutional quality, financial development, market size and policy environment etc., we include the country-year fixed effect γit. The sector-year fixed effect is θjt is included as a control to exclude the effects of price volatility and sectoral shocks. The comparative advantage of a country varies, and it determines the production cost of a sector in each country. Government subsidies and taxes for the same sector in different countries could differ. To take care of these issues, we control for the country-sector fixed effect μij.

To explore how GVC participation shapes the effects of FDI, we include an interaction term to equation (1) and the specification becomes: lnyijt = β0 + β1 × lnFDI ijt + β2 × lnFDI ijt × GVCijt + β3 × GVCijt + γit + θjt + μij+εijt, (2) where _GVCijt_ is the GVC participation of country _i_ sector _j_ in year _t_ .

# 5.2 FDI has higher spillovers for sectors with lower GVC participation

This section reports the empirical results to show the direct effect of FDI on a sector’s productivity and the role of GVC participation in shaping this effect. The main results consider average labor productivity at the 35-sector level according to the MRIOT (see appendix table).

## **Main results**

The main empirical results are reported in Table 3, where the dependent variable is log of sector labor productivity. In column 1, the coefficient of _ln FDI_ is positive yet statistically insignificant. This result is consistent with most of the previous literature, in which there is ambiguity on the direct effect of FDI. When we include the interaction term to investigate how the GVC participation shapes FDI effects (column 2),
degrees increased by 41 percent, and student-teacher ratios (STR) fell by 16 percent. The fraction of teachers not paid on time fell from 51 to 22 percent, and the fraction of teachers aware of recognition programs increased from 50 to 81 percent. Finally, the frequency of school inspections and parent-teacher association (PTA) meetings increased significantly.

However, reductions in teacher absence rates were more modest. The all-India weighted average teacher absence in rural areas fell from 26.3 to 23.6 percent.<sup>3</sup> While increased teacher hiring brought the STR down from 47 to below 40, the effective STR (after accounting for teacher absence) was still over 50 (having reduced from 64 in 2003 to 52 in 2010). The variation in teacher absence across states remains high. At one end, several top performing

> 2These come from the “District Information System for Education” and are commonly referred to as the DISE data.

> 3The all-India weighted average teacher absence estimated in 2003 was 25.2 percent; the corresponding figure for the rural sample was 26.3 percent. The panel survey only covered the rural sample.

2
DIALOGIC READING AND STORYBOOK EVALUATION IN KENYA 14

# **Study Design**

Caregivers who completed the baseline survey were stratified by community and grouped into geographic clusters containing either one or two eligible caregivers each. Within each community, caregivers who completed the baseline were blocked by the number of caregivers within the geographic cluster; clusters were then randomly assigned to either the control group or one of four treatment arms (Figure 1). The study design was preregistered through the ISRCTN registry (id: ISRCTN68855267) accessible at: <u>https://doi.org/10.1186/ISRCTN68855267. Study</u> procedures were approved by local and international human subjects review boards.

The treatment arms were: T1: Storybooks: Households received a packet of 6 children’s storybooks – two books in English, two in Luo, and two in Swahili.

T2 = T1 + Training: In addition to the T1 provisions, caregivers were invited to attend a modified dialogic reading training; they also received several text message (SMS) reminders encouraging them to read with their children in the week following the training session. T3 = T2 + Booster: In addition to the T2 provisions, households received a follow-up “booster” training session two weeks after the initial training.

T4 = T3 + Home Visit: In addition to the T3 provisions, households received a home visit from one of the dialogic reading trainers approximately one week after the initial training.

Control: Households randomly assigned to the control group did not receive storybooks, training, or home visits between the baseline and follow-up assessments. Households were informed that a second training session would be held approximately two months later. These sessions were held in each community after follow-up data had been collected. Both caregivers assigned to the control group and those who were not included in the baseline sample were invited to attend.
%|FEX rate, fuel prices.|
|Vietnam|89%|86%|91%|2.1%|n.a.|

_Notes:_<sup>a</sup> Data for Morocco is only available for one year (2013).<sup>b</sup> Coefficient of variation (standard deviation divided by the geometric mean). _Source:_ World Bank staff.

# **#3: Losses due to the under-recovery of costs are absorbed by the sector in different ways, and some countries manage to maintain utilities’ financial viability better than others**

29. The case studies illustrate that there are different mechanisms to absorb the under-recovery of costs, with different implications for the utilities’ ability to adequately serve their customers. Table 4 summarizes indicators of how losses are absorbed. Notably, government support falls short of restoring utilities’ financial viability in almost all cases.

15
layers)**|The state is an indirect owner through another firm/entity. In this case, B is<br>an indirectly owned BOS as the firm A (with 25% direct participation of the<br>MoF) owns 10% or more of company B.|
|**Indirect (3 layers)**|The state is an indirect owner through ownership links that relate three firms.<br>In this case, C is an indirectly owned BOS if the firm B owns 10% or more<br>of company C, which is owned (by 10% or more) by company A, which is<br>owned by the state. So, MoF owns A, which owns B, B owns C. Therefore,<br>C owned by the government through at least 3 layers of ownership.|
|**Indirect (4+ layers)**|Firms indirectly owned by the state such that four or more firms are<br>identified in the ownership path that connects the firm D with the Public<br>Authority. For example, Public Authority owns A, which owns B, which<br>owns C, and it owns D. D is indirectly owned by the government as it is<br>connected to a Public Authority through a chain of four (or more)<br>participatedfirms.|

Source: Author’s elaboration using World Bank’s BOS database Results in Table 14 are indicative of indirectly owned BOSs being associated with better performance results when compared to directly owned BOSs. As the distance from the public authority increases, BOSs appear to achieve better performance outcomes. Compared against directly owned BOSs (reference category), column (1) indicates that indirectly owned BOSs are 28% more productive.<sup>25</sup> When the degree of separation from the state increases, which we consider as a proxy of autonomy,<sup>26</sup>
removed from the database after at least five years of non-reporting.<sup>22</sup> It is therefore impossible to distinguish between firms that exit the dataset due to failure or for some other reason, such as employment reduction or merger. The analysis is therefore focused on a balanced sample of surviving firms—firms present for the entire date range. We note that this precludes productivity growth due to firm entry and exit and could imply that the sample firms are likely to be more productive than the population average because firms too unproductive to survive drop out.

Table 1 shows the final samples’ compositions and compares their derived aggregate labor productivity growth rates with those derived from Eurostat for 2003–2007, the years for which Eurostat and Amadeus overlap. Evidence suggests that the samples mirror productivity trends at the macro level, lending credence to the use of micro data to explain macroeconomic growth.<sup>23</sup>

**Table 1 Aggregate Annual Productivity Growth, 2002–2007: Amadeus and Eurostat**

|||**Sample**|**1(10+ empl**|**oyees)**<br>**Serv**|**ices**||**Sample**|**2(50+ empl**|**oyees)**<br>**Serv**|**ices**|
|---|---|---|---|---|---|---|---|---|---|---|
||**Obs**|**Manufa**|**cturing**|**(w/o cons**|**truction)**|**Obs**|**Manufa**|**cturing**|**(w/o cons**|
where _dit_ is the Polity2 democracy index of country _i_ in period _t_ . The lagged value of this variable on the right-hand side is included to capture the persistence of democracy. The parameter _NRAit−1_ is the lagged value of the protection level in agriculture. Other covariates are included in the vector _Xit−1_ . The parameters _μt_ and _δi_ denote full sets of year and country fixed effects, respectively, and ε _it_ is an error term capturing all other omitted factors.

For the same reason given above, the model in equation 3 is estimated using D-inD<sup>27</sup> and first-difference GMM estimators. Moreover, because democracy is a persistent variable, we run a system GMM regression (see Arellano and Bover 1995). We find that the lagged protection coefficient is always insignificant in these additional regressions (see the results in table S.4 in the supplemental appendix). Thus, these results suggest that there is no feedback effect of agricultural protection on the transition to democracy.

# **<<B>>** _Definition of regime change_

The evidence presented thus far has been obtained from approximately 40 political reform episodes based on the Polity2 index. We have checked whether our results are driven by the specific definition of our political reform variable. We have employed three alternative approaches: (a) defining a democracy variable using _all_ of the 67 reform episodes from Polity2; (b) using the data from Papaioannou and Siourounis (2008) and including only 23 (permanent) democratization episodes; and (c) only considering permanent transitions from Polity2, namely, those that lasted at least eight years. These democratization dummies differ in terms of not only the number of regime transitions considered but also the timing of the reform episodes (see table S.1).

The results are presented in table S.5 in the supplemental appendix. The results remain robust using these different measures of democratic transitions. Moreover, the additional regression results suggest that permanent transitions are most important. In line
nomic effects for Colombians, and had low media and political salience. These factors suggest that the PEP program could have low to negligible effects on electoral outcomes. The would be if voters the as only potential exception perceive regularization program imposing a higher fiscal burden on migrants. However, evidence indicates that the fiscal net cost of a regularized migrant household is actually _lower_ than that of an undocumented migrant household (Ib ́a ̃nez et al. 2024).

# **IV DATA**

Our main empirical analysis employs municipal-election panel data between 2000 and 2022. Our sample is restricted to this period since municipal-level voting roll registries are available from 2000. We also did a survey experiment to clarify the mechanisms underlying our main results. Details on the survey are presented in section VII. A.

. **_PEP take-up_** The number of individuals who applied for the PEP program is available by municipality. Colombian migration authorities provided the data, which is illustrated 1. in Figure 15
They will select data that are believed to be valuable after accounting for information processing costs (Handel and Schwartzstein, 2018).

We report effect estimates of two components of an information campaign in relation to a profitable and climate-friendly technology investment in rural Senegal. One component provided information about the benefits and quality of solar lamps in a general way, while another singled out for each of the main applications the most suitable type among a large variety of solar lamps available at local markets and the associated benefit. The significant advantage of the second component is a reduction in data processing costs through simplification of choices.

We find that the provision of information on optimal lamp types was required to increase adoption of solar lamps on the extensive margin. However, providing information about general benefits and quality was sufficient to increase the number of owned solar lamps among existing customers (intensive margin).

In our mechanism analysis, we further show that the generic information component does not seem to affect any intermediate variables in the extensive margin sample, whereas the addition of information about optimal product types significantly increases the perceived quality of solar lamps. The only exception are the results for the locus of control measure that arguably captures the extent to which households felt overwhelmed with translating information of the campaign into optimal investment. Here, we find significant effects of both campaign components. However, while the provision of generic information was negatively correlated with the measure of locus of control, information that simplified the choice set removed this negative effect. Among early adopters of solar lamps, results are different. We show that the generic information intervention is positively correlated with quality expectations. Hence, existing consumers update priors as a result of the generic information.

A simple learning model of selective attention along the lines of Hanna et al. (2014) is presented to rationalize the experimental data. Households face an abundance of potential features that might affect home production but cannot possibly attend to everything because paying attention is effortful. We argue that, in our experiment, households did not pay
Preferences over policy might change not only with age but also across cohorts. For example, if younger generations are more supportive of increases in government spending on the environment, they may also be more supportive of these policies when they grow old than the current generation of old individuals. In other words, the age patterns that emerge in a cross‐sectional survey could be a mix of both life cycle effects and preferences changing across generations.

Empirical evidence underscores the importance of controlling for age or cohort effects. To our knowledge, the only paper that attempts to disentangle age, cohort and time effects in attitudes towards public spending priorities in European economies is Sorensen (2013). Using data from the International Social Survey Program (ISSP) – which comprises four repeated cross‐sectional sample surveys for the years 1985, 1990, 1996 and 2006 covering 22 countries, 16 of them in Europe – the author finds that people do shift their public spending priorities over the life cycle, but not by as much as cross‐sectional age comparisons would suggest. In particular, older people tend to be less (more) supportive of an increase (decrease) of government expenditure on education (pensions) than their younger peers. However, controlling for cohort effects significantly reduces, but not entirely eliminates, these age patterns. As mentioned in the literature review section above, one limitation of the data used by Sorensen (2013) is that the alternatives available in the questionnaire for additional government expenditure are not mutually exclusive.

To disentangle cohorts and age effects we need a panel data set covering a sufficiently long time period or a pseudo‐panel of repeated cross‐sectional data that would allow us to follow the same individuals or group of individuals over time. As far as we know, the best data set to meet these requirements is Eurobarometer, from which we use repeated cross‐sections with annual frequency between 2004 and 2013 for 27 European countries.<sup>9</sup> An advantage of this data set is that
/sup> Competition is at the district level, with state governments choosing a single provider for each district. Private insurance companies have been allowed to offer voluntary cover since 2007 and cover to borrowing farmers, for whom insurance purchase was compulsory, from Rabi 2009-10. For the 2009-10 agricultural year, private insurance companies sold policies amounting to over 20% of the entire WBCIS portfolio by premium volume, with the remainder sold by the public insurer AICI.

With only four years of matched premium and claim payment data it is not possible to make strong statements about the loss ratio, the ratio of claim payments to commercial premium since it is challenging to differentiate between favorable weather experience and expensive products. Over the 2007-2010 agricultural years, the unweighted average loss ratio was 65% and the unweighted producer loss ratio, the ratio of claim payments to farmer premiums, was approximately 130%; that is to say that for every ₹1 of farmer premium paid in each of the four years, ₹1.3 of claims were paid in each of the four years. This is lower than the producer loss ratio of 300% for borrowing farmers purchasing NAIS cover between 2000 and 2008 (Mahul et al. 2008).<sup>5</sup> However, with only four years of claims data, the most recent of which happened to be among the best of the past decade, this may not be representative of the future average producer loss ratio.

# **3. Weather insurance product design**

The primary aim of designing an index for agricultural insurance should be to minimize basis risk, the risk that the index does not accurately capture farm-level losses. In particular, it is important to ensure that the product makes appropriate claim payments in years that are unusually bad for

> 4 Under the modified NAIS private sector insurance companies also compete with the public insurer.

> 5 The producer loss ratio for nonborrowing farmers purchasing NAIS cover between 2000 and 2008 was 640%. This disparity illustrates the impact of adverse selection
of Bangladesh in 2013, which is lower than 2,854.1 kcal per AME (age between 30 and 59) with a moderate activity level, found by Waid and others in their study in 2017.

> 10FAO. FAOSTAT Trade Matrix database. Available at: https://www.fao.org/faostat/en/#data/TM, accessed 24/12/2021.

> 11 FAO. FAOSTAT Food Balance Sheet. Available at: https://www.fao.org/faostat/en/#data/FBS, accessed 24/12/2021.

8
Policy Research Working Paper 9619

# **Abstract**

This paper examines whether a country’s participation in global value chains supports spatial convergence in the domestic economy. In theory, production disintegration through “unbundling” makes industrial development less lumpy, providing opportunities for smaller cities to plug and play in niche spaces while not having to fight the agglomeration economies offered by large metropolitan areas. Using data on the size distribution of cities within countries and the Organisation for Economic Co-operation and Development’s Trade in Value Added database, the paper finds that integration in global value chains is strongly associated with greater concentration in large urban agglomerations, not less. A unit standard deviation increase in domestic value added in exports of intermediate products is associated with a decline of 0.1 standard deviation in the Zipf coefficient, an index measuring spatial dispersion. Spatial concentration is strongest for global value chains involving knowledge-intensive business services and high-technology manufacturing.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Practice and the Finance, Competitiveness and Innovation 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 agrover1@worldbank.org and slall1@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
> 7 Elizabeth L Prado, Kathryn G Dewey; Nutrition and brain development in early life, Nutrition Reviews, Volume 72, Issue 4, 1 April 2014, Pages 267–284, https://doi.org/10.1111/nure.12102

> 8 <u>http://blogs.worldbank.org/developmenttalk/adversity-gets-brain</u>

> 9 WHO’s “igrowup” package for R was used to compute HAZ scores for all children in the NNS anthropometry dataset. https://www.who.int/childgrowth/software/en/

> 10 United Nations Children’s Fund (UNICEF), World Health Organization, International Bank for Reconstruction and Development/The World Bank. Levels and trends in child malnutrition: key findings of the 2018 Edition of the Joint Child Malnutrition Estimates. Geneva: World Health Organization; 2018 Licence: CC BY-NC-SA 3.0 IGO. https://www.who.int/nutgrowthdb/2018-jme-brochure.pdf?ua=1

> 11 Since almost all stunting occurs within the first two years of life, the first “1,000 days” between conception and a child’s second birthday has become a leading organizing framework for nutrition interventions (https://thousanddays.org).

> 12 WHO Multicentre Growth Reference Study Group. WHO Child Growth Standards: Length/height-forage, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: Methods and development. Geneva: World Health Organization, 2006. https://www.who.int/childgrowth/standards/technical_report/en/

> 13 United Nations Children’s Fund (UNICEF), World Health Organization, International Bank for Reconstruction and Development/The World Bank. Levels and trends in child malnutrition: key findings of the 2018 Edition of the Joint Child Malnutrition Estimates. Geneva: World Health Organization; 2018 Licence: CC BY-NC-SA 3.0 IGO. https://www.who.int/nutgrowthdb/2018-jme-brochure.pdf