rafmacalaba/data-use-mentions-extended huggingface dataset:
# **I. Introduction**

Women’s empowerment has been widely promoted as a key development goal. It is not only desirable in itself, but also has been linked to faster economic growth (World Bank 2011). When women have greater bargaining power to make household decisions, children’s education and health outcomes are better and the well-being of families improves (Duflo 2012). Giving women more power also changes communities’ choices in important ways, often resulting in higher overall efficiency of the society (Udry 1996; Goldstein and Udry 2005). Recognizing the importance of women’s empowerment, the United Nation included “Achieve gender equality and empower all women and girls” in its 17 Sustainable Development Goals. The World Bank too has made gender mainstreaming a priority in development assistance, identifying women’s empowerment as a key pathway to sustained poverty reduction and shared prosperity.

Empirical studies show that electrification disproportionally benefits women and girls. In households with electricity, women spend less time on household chores and are more likely to participate in income-generating activities, and girls have higher educational attainment (Samad and Zhang, 2016, 2017, 2018; Kohlin et al. 2012; IEA 2008). What is less understood is whether improvements in welfare outcomes also empower women and change gender norms and practices inside households.

This paper examines whether electricity access enhances women’s empowerment at the household level, defined as their ability to make strategic life choices, control resources, and craft decisions that affect important life outcomes (World Bank 2002). To the best of our knowledge, it is the first paper that provides empirical evidence on the causal link between electricity access and a wide range of empowerment indicators. The analysis is based on data from the India Human Development Survey (IHDS) carried out in 2011/2012. This survey provides a broad range of information on women’s involvement in household decision making. The survey covers more than 40,000 Indian households, including more than 200,000 individuals
either ignorant of, or vastly overestimated the costs of registration. We might therefore have expected that simply informing firms about the costs of registration would be sufficient to induce registration. But we find that information and household-based nationally representative labor survey. The Bangladesh survey data come from a census of 55,817 firms in randomly selected sampling areas from 19 districts conducted by the World Bank in 2009-2010.

> 11 Our results are also consistent with evidence from an unpublished parallel experiment in Lima, Peru which randomly encouraged firms to obtain a municipal license (Jaramillo, 2009; Alcázar et al, 2010). About one-quarter of firms offered information and reimbursement of direct costs obtained the municipal license. One-third of those who didn’t register reported problems with other regulations such as zoning, consistent with our finding that land issues prevent many firms from registering. A follow-up survey 18 months after baseline failed to find any significant impact of registration obtaining the municipal license on firm size, access to credit, or profitability, but their power to detect an effect is low. Moreover, since banks and the government typically require a tax license rather than municipal license as proof of formality in many countries, there may be less ex ante reason to expect a positive impact on firms of municipal registration.

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The magnitude of these effects is large relative to the existing literature.<sup>10</sup> Two features of our setting may help explain this large magnitude. First, village governments in Indonesia exercise considerable discretion over locally managed services, so leadership changes can have direct and immediate consequences for service delivery. Second, our index captures services such as garbage collection and street lighting, where substantial improvements may be achieved through relatively low-cost organizational changes rather than major capital investments. As a balance check, the last column of Table 7 presents the same service provision index from the 2014 _Podes_ wave, excluding garbage collection, which was not recorded that year (see also Figure 5, panel b). Prior to the most recent village head turnover, service provision was 0.06 s.d. lower (non-significant) in turnover villages.

**Heterogeneity by Nepotism.** Our findings align with qualitative evidence emphasizing the crucial role new village heads play in shaping local development outcomes.<sup>11</sup> In particular, the staff replacements and reduction in nepotism we observe may contribute to the emergence of more responsive local bureaucracies. Officials in turnover villages exert greater effort to engage with citizens and understand their priorities, fostering investments aligned with citizen preferences and ultimately improving service provision—an effect we observe in both administrative data and citizen surveys.

In Appendix Table A.15, we provide evidence that the positive effects of turnover are driven by villages where the electoral outcome disrupts nepotistic networks. An important caveat is that the measures of nepotism used to partition the sample are potentially endogenous. In columns 1–2, the split is based on whether the current village head employs a relative, which is itself directly affected by turnover (Table 4). Bearing this in mind, the improvements in service provision appear to only occur in villages where the village head does not employ a relative. In these villages, turnovers lead to a 0.77 s.d. increase in service provision (
# **Figure 1. Distribution of natural disasters over time and across regions**

A. Intensity of natural disasters <!-- Start of picture text --> Percent of population Percent of population<br>Total affected Intensity shock<br>Total deaths (RHS)<br>7 0.025<br>6<br>0.020<br>5<br>4 0.015<br>3 0.010<br>2<br>0.005<br>1<br>0 0.000<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br><!-- End of picture text --> <!-- Start of picture text --> B. Intensity of natural disasters, by region<br>Percent of population Percent of population<br>7 Total affected Total deaths (RHS) 0.007<br>6 0.006<br>5 0.005<br>4 0.004<br>3 0.003<br>2 0.002<br>1 0.001<br>0 0<br>AME EAP ECA LAC MNA SAR SSA<br><!-- End of picture text --> _Sources:_ EM-DAT, WDI (Population data).

_Note:_ The average of affected as a ratio to population, death to population and intensity shock across countries in a given year (in panel A) and across countries in a given region over 2000-22 (in panel B).

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health facilities are at locality, and therefore would be difficult to have both treatment and control groups in the same small locality. In selecting localities, the probability of being chosen is population weighted. This means that the PROGRESA household data subsamples are self-weighting. Since PROGRESA is targeted primarily at poor families, criteria for selection into the experiment are based on empirical measures of poverty within the locality. After selecting a subset of localities to participate in the evaluation, it was determined using poverty-related criteria, which households within each locality would be eligible to receive PROGRESA services. Finally, each locality is randomly assigned to be a member of either the treatment or control group. In localities assigned to treatment, all eligible households within the locality are offered and usually take advantage of PROGRESA benefits/services. In localities assigned to the control group, none of the households receive PROGRESA benefits/services. There was a 60 percent probability of being assigned to treatment and a 40 percent probability of being assigned to the control group; 320 localities ended up being assigned to the treatment group and 185 to the control group.

# **6. Descriptive Statistics**

Child labor is higher among indigenous-monolingual populations.<sup>18</sup> Child labor incidence decreased from 16.3 percent to 7.4 percent between 1997 and 1999 (see Table 1). An overall decrease is noticed, which is higher among indigenous-monolingual children, while Spanish-monolingual children’s labor supply decreased 8.2 percent, it decreased 8.9 percent among indigenous-monolingual children. As children grow older, child labor increases. The steepest change occurs in 1997 in the indigenous-monolingual group from 13 to 14 years with a 13.3 percent increase (see Table 2 and Figure 1). In 1997, indigenous-monolingual children have higher child labor incidence in all age groups except 16. Bilingual children have the lowest child labor incidence in all groups in 1997. The indigenous-monolingual group has a higher incidence for children between 8 and 12 years. An increasing child labor with
Policy Research Working Paper 9219

# **Abstract**

Argentina’s private investment in research and development is well below that of its peers. One important reason may be low and very heterogeneous returns to research and development activities on productivity. This paper uses novel microdata to estimate the returns to research and development and understand the contextual factors that shape their heterogeneity. The paper groups these context-based factors into knowledge complementary factors (that is, factors that affect the returns via learning capabilities from external sources of knowledge) and market complementary factors (factors that act via business capabilities to appropriate the returns to research and development investments). The paper hypothesizes that the effects of contextual factors depend on firms’ management capabilities and attitudes (innovative capacity), which determine firms’ ability to benefit from the context. The findings suggest that the returns are indeed heterogeneous across regions and sectors, and these results depend on some context-based factors, which can boost or depress the returns to R&D. The results have important policy implications, considering the effectiveness of innovation policies, need for adapting to specific regions and sectors, and maximization of the impact of these factors on the returns to research and development.

This paper is a product of 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 xcirera@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.
<!-- Start of picture text --> % of Baseline % of Baseline<br>1 7<br>a) 6<br>8<br>7 5<br>64<br>5<br>4 3<br>32<br>2<br>!<br>d<br>0 0<br>1990 1995 2000 2005 2010 2015 2020 1990 1995 2000 2005 2010 2015 2020<br>—— Average Emissions ———— Phaseout Schedule —— Average Emissions ——— Phaseout Schedule<br>% of Baseline % of Baseline<br>1.5 90<br>1 80<br>70<br>5<br>60<br>0 50<br>-5 40<br>4 30<br>20<br>-1.5<br>10<br>2 0<br>1990 1995 2000 2005 2010 2015 2020 1990 1995 2000 2005 2010 2015 2020<br>—— Average Emissions——— Phaseout Schedule —— Average Emissions——— Phaseout Schedule<br><!-- End of picture text -->
# Data and methods

The experiment was embedded in the Listening to Central Asia series of surveys in the Kyrgyz Republic, Uzbekistan, and Kazakhstan, surveys that are designed to continuously monitor wellbeing and views on topical policy issues.<sup>6</sup> The surveys are collected from a single (“omniscient”) household member, and household selection was designed for national representativeness.

Fieldwork was conducted in two phases. In the first phase, a national in-person household survey was conducted in each country using regionally stratified two stage cluster sampling procedures. In the second stage, a randomly selected sub-sample from the first stage in each country was recruited in a monthly panel survey using a phone-based method conducted by private survey companies under the direction of World Bank staff. The sub-samples were each nationally representative and for the present study include a total of 6,783 individual responses (in Uzbekistan, the Kyrgyz Republic, and in Kazakhstan). Data collection for the survey experiment was conducted in May-June 2021.

For the core experiment, respondents were assigned a set of 8 separate vignettes, each with three randomly generated components of the form: “A <RANDOM AGE>-year-old <RANDOM GENDER> works as <OCCUPATION>. <HER/HIS based on RANDOM GENDER> monthly gross earnings total <RANDOM MONTHLY PAY> (before taxes and extra charges).” Following each vignette, the respondent was asked: “Is this person severely overpaid, overpaid, fairly paid, underpaid or severely underpaid.” The content of the randomly generated portions (including all components set off by “<>” in the example above, apart from occupation) followed the design described in Table 1. Every respondent was asked similar questions over a fixed set of eight occupations, including: store clerk, farm worker, receptionist, software engineer, manager, medical doctor, taxi driver, and schoolteacher.

**Table 1: Randomized elements of the experiment design**

|**Dimension**|**Values*
households (Stephens and Barrett, 2011). They can push up the seasonal price gap well beyond the levels expected in settings with well‐functioning markets.

Excess seasonality in prices may further translate into seasonal variation in dietary intake and nutrition, for example, when households are credit constrained or ill‐equipped with other coping strategies, as has been documented in Ethiopia (Dercon and Krishnan, 2000), Bangladesh (Khandker, 2012), and Tanzania (Kaminski, Christiaensen, and Gilbert, 2016).<sup>6</sup> Moderation of seasonal price variation (for example through facilitation of storage or access to credit) could then be a way to increase overall food and nutrition security.

A second reason for refocusing attention to food price seasonality relates to the sharply increased volatility of world food prices in the immediate aftermath of the 2007‐08 world food crisis (Gilbert and Morgan, 2010, 2011), although volatility levels appear to have dropped back since that time (Minot, 2014). This volatility was transmitted to a greater or lesser extent to food prices in developing countries and attracted considerable government attention (Galtier and Vindel, 2012; World Bank, 2012; Ceballos, Hernandez, Minot, and Robles, 2015). Food price volatility arises from both international and domestic shocks to production (harvest shocks) or consumption (changes in purchasing power). However, seasonality (i.e. known fluctuations) also contributes to price volatility (especially domestically) and would require different policy instruments to address it. Little is known on the extent of this possibility.

The third reason relates to the measurement and analysis of poverty (the focus of the first Sustainable Development Goal). Poverty measurement relies heavily on food expenditure information, which is typically collected only once for each household during at a particular point during the year (with a 7 to 30 day recall period). The annual expenditures measures derived from these surveys will be incorrect when food price seasonality is substantial and not corrected for
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analysis proceeds in three steps. In Section 5.1, we analyze how correlations between displacement status and political engagement change over time within Uganda. In Section 5.2, we use matching models (Ho, Imai, King & Stuart 2007) to grapple with non-random patterns of displacement and the context of displacement in the Philippines. Finally, in Section 5.3, we again use matching models to grapple with potential selection effects in Iraq.

# **5.1 Uganda**

We analyze two waves of repeated cross-sectional data in Acholi, Uganda. We plot the trends according to our survey data in Figure 8. We collect two measures of empowerment consistently across the two survey waves: Relationship with the Community and Contribute to Events in Community. As such, we restrict our analysis in Uganda to those variables.

We plot aggregate trends in these two measures across the survey waves in Figure 8. In general, the trend-line improved between the survey waves, while the number of respondents who reported ever being displaced declined modestly. In aggregate, respondents in Uganda consistently report strong perceptions of their relationships with their communities, limiting the variation available to understand how experiences of displacement are associated with these relationships. There is important variation in whether respondents feel they are able to contribute to events in their communities both within and across survey waves, however.

To explore how the relationship between individual experiences with displacement and empowerment vary over time, we run a set of four logistic regressions, which we plot in Figure 3b. The sampling is consistent between surveys, but we do not re-sample the same people (therefore it is not a panel), we therefore run separate models for the 2007 and 2010 data. In the top models, we use a binary indicator for whether respondents indicate that they have either “Good” or “Very Good” relationships with their communities as our dependent variable. In the bottom two models, we use a binary indicator for whether respondents agree with the statement that they are able to
# **1. Introduction**

Women’s participation in the economy of Bangladesh has increased steadily since the country’s independence. Female labor force participation (FLFP) grew from just 4 percent in 1974 to 8 percent in 1984, before jumping to 24 percent in 2000. This trend has continued over the past two decades, partly attributable to a ready-made garment sector that accounts for 80 percent of total export earnings and employs 4.4 million people, including more than 3 million women (Fathi 2019, World Bank 2012a).<sup>1</sup> Yet, at 36 percent in 2017, FLFP in Bangladesh is still below the world average of 47 percent (World Bank 2020). In addition, FLFP has consistently been lower in urban areas than in rural areas and has declined in recent years despite economic growth and increases in women’s education. Between 2010 and 2017, urban FLFP fell from 34.5 to 30.8 percent, while rural FLFP increased from 36.4 to 38.5 percent (BBS 2018).

Many explanations have been offered for the low FLFP, ranging from underreporting of women’s home-based work in labor market surveys (Mahmud and Tasneem 2011) to social and cultural factors that traditional labor market studies omit, such as norms about women’s safety and seclusion (Solotaroff et al. 2019, Kotikula et al. 2019, Węziak-Białowolska et al. 2020). It is also widely recognized that women’s employment is mediated by their roles in the reproductive and care economy and by the patriarchal norms that define their role in society (Khatun et al. 2014). In Bangladesh, women’s care responsibilities increase substantially after marriage and childbirth. Marriage is universal, and the average age at first marriage, 15.9 years (BDHS 2014), has increased very slowly over the past few decades (Zahangir 2015, Shuler and Rottach 2010). The average age of first childbirth is 18; women in the
are modeled as numeric digits of the same unit as the dependent variable, e.g. a monthly volatility of 2% enters the regression as a value of 0 _._ 02. The simple results highlight that prediction performance is not significantly related to data dimensions of the problem, instead high volatility and high inflation are determinants of imputation accuracy. Jointly, these two variables explain almost half of the in-sample variation. The signs of the coefficients suggest that when volatility increases, prediction performance deteriorates and data collection becomes more important. When the inflation trend is strong relative to volatility, then the price trend is clearer in the data and imputation accuracy increases, suggesting that robust inflation tracking is possible even when little ground truth data is available. _Source:_ The statistics have been prepared by the author for this paper based on end-of-August (2021) food price data from World Food Program.
to the fact that these estimates are based on the most recent (version 6) GTAP data base, which is further updated to account for EU enlargement as well as the WTO accession of China and others. These recent trade reforms have reduced the overall level of protection worldwide – thereby lessening the gains from reform. In addition, the version 6 data base has a complete treatment of preferential tariffs – including the EU’s 2001 Everything But Arms initiative, which means that gains to the least developed countries from trade reform are considerably reduced.

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**Table B.9:** Water and Sanitation

||Median|Mean|SD|Min|Max|N|BL data|
|---|---|---|---|---|---|---|---|
|(1) Water and sanitation HH index|0.043|0.050|0.987|_−_5.667|6.236|5053|Yes|
|(2) Water and sanitation village index|_−_0.012|0.098|1.012|_−_1.212|3.791|5053|Yes|
|(3) Satisfaction with village sanitation index|0.290|0.050|0.973|_−_3.415|1.885|5051|Yes|

The household index is comprised of water sources for drinking, for other uses, the existence and type of toilet facilities and the actions households take to make water safe to drink. The village index contains the existence and types of water sources for drinking and general use as well as the existence of public toilets and waste facilities. The satisfaction index consists of questions about sanitation and health services offered as well as village cleanliness. †: dummy variable; §: likert scale (1-4); _¶_ : likert scale (1-5); _♦_ : measured across all health episode types (ante and post natal care, child birth, under-2 vaccinations, and illness and injury); w , 1 , 6 , and 12 stand for measures referring to the last week, one month, six months and twelve months respectively.

**Table B.10:** Economic Outcomes

||Median|Mean|SD|Min|Max|N|BL data|
|---|---|---|---|---|---|