rafmacalaba/data-use-mentions-extended huggingface dataset:
7" The price of the same brand contraceptive such as Hicrogynon, probably the most popular pill in the region, ranges from $2.57 in Grenada to $3.33 in St. Lucia, 30 percent higher in St. Lucia. Orals are cheapest in Grenada for all brands, except Diane, which is the same price on both islands that carry the product, and is more than twice as expensive as any other method. Diane is available only with a prescription. Perle, at less than a third the price of other orals in St. Lucia, is the social marketing product distributed through a USAID funded project.

Condom prices vary widely based on quality and consumer villingness to pay It is hard to compare these prices since each country carried different brands. The available products at the time of the survey vere quite different, buit no information vas available on typical periods.12 The data on injections and foam tablets are only available for.a single country, and these are useful in comparing NGO and private prices for similar contraceptive products. Table III.13 summarizes the prices charged by local family planning associations (FPA) in Antigua, St. Lucia and Grenada for the methods each offers. In St. Lucia, the FPA's industry-based program offers a vider range of products and higher prices than its family planning center, thus there are four columns in the table.

As in the case of the for-profit sector:, pr- es of contraceptives vary considerably across islands. Ironically, the Grenada FPA has the highest prices for oral contraceptives, although its pharmacies had the lovest commercial prices among the countries for vhich data were available. In the St. Lucia industry-based program, a number of brand name products are offered at concessionary prices, although only one of the three oral contraceptives is priced 12. Shopkeepers indicated lov demand for condoms among local couples.

Ml.tp:Fafily Planning **Paper** (whole)
NUES/MES panel allows further adjustment for imputed return on the value of capital (tools, inventories, and location if owned), and hours worked by unpaid workers (table **3).10** The results would appear to invert the conventional view of the relation between formal and informal sectors, regardless of the definition chosen: movement into formal salaried employment is associated with a significant _decline_ in remuneration, except from informal salaried employment; movement from formal salaried employment always leads to a significant _increase._ However, in the absence of information on the value of benefits, compensation for risk, the value of independence, or in-kind payments or implicit training costs, the magnitude of the distortion-free differential cannot be known ex-ante, and neither these, nor any previous reported sectoral differentials are reliable measures of segmentation. Further, though table 3 suggests that those reporting voluntary moves into self-employment do far better than those moving involuntarily, we cannot necessarily interpret asymmetries in differentials in tables 2a and b as evidence that there is a larger component of voluntary movement into the formal salaried sector: Involuntary separations due to formal sector firm closures imply the loss of well paying jobs and a large differential, while the analogous failure of a micro-enterprise may imply low earnings prior to transition and a smaller differential.

However, the differentials among informal sectors are less affected by unobservables, such as the loss of formal sector benefits, and are suggestive of similarities and differences among them.

> **10** Despite choosing the NUES sample to straddle the MES sample, predictably, the raw differentials differ somewhat. For the critical salaried formal salaried/self-employed transition they are extremely close. For the other cases, although we are dealing with the same relative orders of magnitude, it is less easy to consider these simply as adjustments to the NiJES results reported above. The formal/self-employed differential is slightly, although not significantly higher (11,5 vs 9,4) and the contract/self-employed differential is also broadly similar (10,
log(# MNEs)” represents the natural logarithm of the number of foreign investors, and “log(K)” denotes the natural logarithm of the average capital expenditure. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts the occurrence of the word “uncertainty” from Economist Intelligence Unit (EIU) reports for 143 countries since the 1950s. A higher value indicates greater uncertainty. Uncertainty in year _t_ is approximated as the logarithm of the average number of uncertainty-related words in EIU reports over the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include logarithms of population, GDP and the average exchange rate. Standard errors are clustered at the destination country. Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

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Using data on school enrollment from other children in the household, we document large, positive, and statistically significant impacts on preschool attendance for younger siblings, of about 85% of the estimated treatment impact for target children. This result speaks to the cost-effectiveness of the DICIPE program, as newly built preschools will be there to benefit future generations.

Our paper makes important contributions to the literature in at least three ways. First, our study contributes to recent mixed evidence on the impacts of preschool construction programs in developing countries. Although some studies document positive impacts of preschool construction programs (Berlinski et al. 2008, 2009 in Uruguay and Argentina, and Donald and Vaillant 2023 in the Congo, for example), others find small or null effects (see Blimpo et al. 2022 in the Gambia and Castro and da Cruz 2023 in Brazil). Notably, Bouguen et al. (2018) suggest caution based on the results from a randomized evaluation of a preschool construction program in Cambodia, where the impacts on cognition are negative for the cohort with highest program exposure due to substitution from primary to preschool. Our contribution here is twofold. In addition to showing that the preschool construction program we study in Mozambique led to increased enrollment in primary school and positive impacts on skill formation, we also highlight the unique features of DICIPE that may explain these particularly high impacts, namely the interaction between . the program and the local primary school system _by design_ Second, our research also speaks to the evidence on early childhood development (ECD) programs implemented _at scale_ that aim to improve child development (Bos et al., 2024; Attanasio et al., 2022; Bailey et al., 2021; Sylvia et al., 2021; Attanasio et al., 2014). Existing studies find significant improvements of at-scale ECD programs on child’s cognitive skills. Our paper contributes to these findings by investigating the effects of a program that was implemented through a public-private partnership, which can be a key policy consideration for resource-constrained governments
where _Lhi_ is the subjective land value of parcel _i_ owned by household _h_ , and νhi is an error term. The interaction term of the tenure regime with ownership status is excluded because the estimation is done on a sub-sample of owners for the reason mentioned above. Given the subjective nature of reported land values, and the fact that household characteristics may affect the ability to make the most productive use of land, we also control for household characteristics and expect signs of the coefficients on the different variables to be consistent with what had been obtained earlier.

# **3. Context and descriptive evidence**

To provide the context for our empirical analysis, this section highlights the importance of tenure security for investment and increased agricultural productivity for poverty reduction in Uganda. It describes the historical events that have significantly reduced tenure security for a large number of occupants as well as the measures taken by the recently passed Land Act to restore such security. We note the vast gap between the desired institutional structure and actual levels of implementation and use our survey data to provide empirical evidence on household and parcel characteristics as well as awareness of different provisions of the Land Act in the population.

# **3.1 The broader relevance of land-related investment**

Although Uganda is a rather small country by African standards, it is characterized by a high level of variation in population density - which ranges from 65 per km<sup>2</sup> in the North to 226 per km<sup>2</sup> in the SouthWest and in physio-geographic conditions, which vary between nomadic semi-deserts in the North-East and the high elevations of the West and South-East. Population growth of 3.3% in the 1991-2002 period considerably increased land scarcity, with an increase in average population density from 64 persons per km<sup>2</sup> in 1980 to 124 in 2002 (Uganda Bureau of Statistics 2005). As more than 85% of the population live in rural areas and agriculture accounts for 77% of
tuition costs aimed to tackle this dimension.<sup>7</sup> Third, bootcamps are male dominated environments. Historic enrollment data provided by the coding schools indicates that, on average, 80 percent of students are male.<sup>8</sup> In order to address this issue, the coding schools in both locations worked closely with to define to make more inclusive. In or- gender specialists strategies bootcamps gender der to increase female participation, this aspect was explicitly highlighted and publicized in the attraction campaigns (explained in detail in section 2.2). Fourth, even conditional on participation, previous pilots of bootcamps identified a prevalent problem of retention for female participants (Hammond et al., 2018). For this, the eligibility criteria (explained in section 2.2 as well) was designed to attract a sample of women with high motivation, willingness to complete the course, and high potential to benefit from it.

Last, logistical issues aimed at increasing program participation were also considered in the design. Participants could choose location, shift, and between two learning modes. Each city counted with two locations of the learning center and three different class shifts (morning, afternoon and evening). One learning mode included mostly face to face classes that lasted at most 3 and a half hours a three times a week. The other one was a day, "blended mode", a mix of face to face and online classes.<sup>9</sup> The part-time design of the bootcamp was key in order to allow participants to continue working or studying another career, if needed.

In Buenos Aires, the bootcamp lasted 20 weeks (with a total of 210 hours of instruction). Most classes started in early April and finished in late July 2019. The last class started in mid-May 2019 and finished in mid-September 2019. In Bogotá, the bootcamp lasted 16 weeks (with a total of 170 hours of instruction). Most classes started in early August
**Figure 1: Enumerator effect: R**<sup>**2**</sup> **and confidence intervals**

<!-- Start of picture text --> 95% Confidence Interval R-squared<br>Marital statusAge Household sizeAlchool and tobaccoAlchool and tobacco (exp)Alchool and tobacco (exp cond)Asset ownershipAsset ownership (cattle)Education levelOpeness to vote for DPOpeness to vote for FDCOpeness to vote for NRMOpeness to vote for UPC<br>.4<br>.3<br>.2<br>.1<br>0<br>Gender<br><!-- End of picture text --> _Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

_Notes_ : The graph reports for each question the R<sup>2</sup> and the corresponding (bootstrapped) 95% confidence intervals for each of the four teams.

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Policy Research Working Paper 9500

# **Abstract**

This paper investigates the relationship between immigration and the exposure of native workers to the health and labor-market risks arising from the COVID-19 pandemic. Using various measures of occupational risks based on European Union labor force survey data, the paper finds that immigrant workers, especially those from lower-income member countries in Eastern Europe or from outside the EU, face greater exposure than their native-born peers to both income and health-shocks related to COVID-19. The paper also shows that native workers living in regions with a higher concentration of immigrants are less exposed to some of the income and health risks associated with the pandemic. To assess whether this relationship is causal, a Bartik-type shift-share instrument is used to control for potential bias and unobservable factors that would lead migrants to self-select into more vulnerable occupations across regions. The results show that the presence of immigrant workers has a causal effect in reducing the exposure of native workers to various risks by enabling the native-born workers to move into jobs that could be undertaken from the safety of their homes or with lower face-to-face interactions. The effects on the native-born population are more pronounced for high-skilled workers than for low-skilled workers, and for women than for men. The paper does not find a significant effect of immigration on wages and employment—indicating that the effects are mostly driven by a reallocation of natives from less safe jobs to safer jobs. .

This paper is a product of the Social Protection and Jobs Global Practice and the Development Research 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://www.worldbank.org/prwp. The authors may be contacted at lbossavie@worldbank.org, dgarrotesanchez@worldbank.org, mmakovec@worldbank.org, and cozden@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange
registered in Dadaab in May 2020. Since then, total case numbers have continuously increased through the first half of 2021 (see Figure 1) to a total of 173,661 confirmed cases and 3,235 deaths as of June 8, 2021.<sup>8</sup> UNHCR reports over 550 confirmed COVID-19 cases among refugees in Kakuma and Kalobeyei and 16 deaths as of end of Q3 2021 since March 2020. The actual number of cases is likely to be much higher than the number of confirmed cases due to limited testing, an overall low vaccination rate (only about 1,000 refugees in both sites having received two doses of vaccine over the same period), and inadequate access to medical facilities and consultations.

In response to the pandemic, the government of Kenya implemented an extensive set of containment measures, including closures of all schools, bars, and indoor restaurants, a ban on international flights, a national dusk-to-dawn curfew, restrictions on public gatherings, limits on public transport passenger capacities and public information campaigns (World Bank, 2021). These measures were effective in all of Kenya from the day they were implemented, including in refugee camps. Although some containment measures were subsequently lifted (Figure 1), Kenya had on average consistently more stringent policies in place than other countries in Sub-Saharan Africa (see Figure 5 in the appendix) which are expected to have had a considerable impact on the socioeconomic lives in national and refugee communities. On March 24, 2021, the country enacted a second lockdown in Nairobi, Kajiado, Kiambu, Machakos and Nakuru counties which entailed closures of schools and other

> 7 For example, in Kakuma and Kalobeyei over 1,200 teachers are employed by UNHCR and partners as incentive workers. They earn five times less than national teachers, while many have similar certification and perform the same job.

> 8 Data on COVID-19 cases in Kenya can be found on COVID-19 Dashboard from CSSE at John Hopkins University (Dong, Du, & Gardner, 533534.).
households. Households in this sample were surveyed at baseline (between April and December 2007), at midline (2009) and endline (2011). As detailed in Section 2, the implementation of the program lasts at most two years – less for some of its components, such as the subsistence allowance which lasts for a maximum of 40 weeks. This implies that by the time the midline survey was conducted, the program implementation was nearly completed in many communities, and by the endline survey it had been completed in all communities for at least 1 year. An additional followup survey was conducted in 2014 and I will use this data to examine the long-run effects of the program for early versus late-treated ultra-poor households.

The household survey measured a number of individual outcomes, including occupa-

> 12More generally, the idea that a risk-neutral employer may provide a risk-averse worker with insurance against income fluctuations is not only limited to the rural labor market (Knight (1921), Baily (1974), Azariadis (1975)).

> 13The districts and communities in which the data was collected were determined by BRAC, as part of their aim to identify the poorest parts of the country. See Bandiera et al. (2017) for details.

10
counterpart in the _Doing Business_ or Global Competitiveness data.<sup>7</sup> We can match four categories of the business environment indicators from Global Competitiveness data and Enterprise Survey data as follows (see table 1 for how each theme of the BE is represented in the three data sets). For Government Protection in the Enterprise Survey data—which includes Corruption Obstacle and Security Cost—we use “Corporate Ethics/Corruption” (based on the survey question, “How do you rate the corporate ethics of companies?”) and “Police System” (based on the survey question, “To what extent can police services be relied upon to enforce law and order?”). For Infrastructure in Global Competitiveness data, we choose “Electricity Quality” (from the International Energy Agency) and “Broadband” (from the International Telecommunications Union). For Human Capital, we choose “Quality of Education System” (based on the survey question, “How well does the education system meet the needs of a competitive economy?”). For Access to Finance, we choose “Financial Development” (the eighth pillar of the Global Competitiveness indicator). For Barriers to Entry, we could not find an appropriate indicator from Global Competitiveness data. All Global Competitiveness indicators range between 0 and 100 points. A higher value of a Global Competitiveness indicator for a country indicates a better business environment.

For _Doing Business_ indicators, we can match three categories of the regulatory environment: for basic Infrastructure (in the Enterprise Survey), we choose the “Getting Electricity” score (from _Doing Business_ ). For Access to Finance, we choose the “Getting Credit” score. For access to land (in the Enterprise Survey) in Barriers to Entry, we choose the “Dealing with Construction Permits” score.

In addition, a relevant indicator outside the scope of _Doing Business_ that comes from the World Bank is the newly minted measure of Human Capital (3), the Harmonized Learning Outcome (
on logarithm of total household consumption expenditure per capita (the first three columns), a binary measure of household consumption poverty status (the second three columns), “started log” of household wealth score (last third three columns), and a binary measure of household wealth poverty status (the last three columns). I use samples restricted to buffers of 50-, 60-, and 70-kilometer from refugee settlement sites. Household sample weights from ESS2 applied. All columns control for household (HH) fixed effects (FEs), round FEs, and 12 time-varying characteristics. The coefficient estimates for these 12 characteristics are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

57
The results from the cross-randomized public and private raffle treatments are inconclusive. Effects of either type of raffle are mostly not statistically significantly different from zero, and the few statistically significant coefficients are inconsistently signed across regressions. For this reason we focus this paper’s attention on interpreting the effects of the “no raffle” savings treatments.

This paper contributes to the burgeoning literature on the effects of formal savings accounts and in this sense is related to the field experiments of Dupas and Robinson (2010) and Atkinson et al. (2010). Dupas and Robinson (2010) offer ordinary savings accounts with de facto negative interest rates to Kenyan urban entrepreneurs, finding positive impacts on investment and income for women. In this paper, by contrast, we explicitly test whether impacts of commitment savings offers are larger than impacts of ordinary savings offers. We also use a very different sample, (mostly) male farmers in rural Malawi. Atkinson et al. (2010) offer microcredit borrowers in Guatemala savings accounts with different features, including reminders about a monthly commitment to save and a default of 10% of loan repayment as a suggested monthly savings target. They find that both features increase savings balances substantially. However, they use administrative records from the lender which restricts the number of observable outcomes and limits analysis of the mechanisms that lead to changes in savings behavior. Our paper is also related to Dupas and Robinson (2011) in seeking to understand the relative importance of various barriers to savings. Dupas and Robinson (2011) do so in the context of ROSCAs, while we provide formal savings facilities.

The remainder of this paper is organized as follows. Section 2 explains the study design and briefly describes the characteristics of the sample. Section 3 explains the estimation strategy. Section 4 presents the main empirical results. Section 5 discusses heterogeneous effects and the mechanisms through which savings accounts may have affected savings and other outcomes. Section 6 concludes.

# **2.** **<u>Experimental design and survey