rafmacalaba/data-use-mentions-extended huggingface dataset:
br>children ages 2–17|
||Non-underweight, 0-17*|Adequate nutrition by weight-for-age measures for<br>children 0–17|

* Available from the EDHS 2000 and 2008 surveys. ** Available from the CAPMAS HIECS 2000 and 2008.

_Nutrition and Hunger._ This group contains three opportunities based on nutrition: non-wasting for children age 0-4, non-stunting for children age 2-17, and non-underweight for children age 0-17. The nutritional indicators measure correct diet and growth. The first indicator, non-wasting or “weight-for-height,” measures the nutrition of children age 0-4. The second, non-stunting, or “sizefor-age” indicator captures _chronic deficits_ in a child’s nutrition and health; it is a _long-term_ indicator. The third, non-underweight or “weight-for-age” indicator captures both short and longterm problems and reflects body mass in comparison to age. It is the most widely used indicator for the measurement of this kind of nutrition problem (WHO, 2007).

The specific measure for each child is compared with a distribution of the same variable for a “healthy” sample, following the WHO Child Growth Standards. For each individual indicator, z- scores are computed as the difference between the measure of the individual and the median of the reference population, divided by the standard deviation of the reference population. WHO classifications of malnutrition are applied: mild (z-score ≤ -1), moderate (z-score ≤ -2), and severe (z-score ≤ -3).<sup>15</sup> Access to the opportunity of nutrition is assigned to all children with _z-scores_ greater than _-1._

> 15 More specifically, the routines ANTHRO and ZANTHRO are applied with statistical software. See O’Donnell, Doorslaer, Wagstaff and Lindelow (2008) and WHO (2007).

9
4.0|0.6|
|Effective tax rate on interest<sup>3</sup>|- 26.5|- 149.5|- 349.0|- 363.8|784.3|- 54.8|49.9|0.0|

I Unweighted average of interest rates on treasury bills and deposits.

2 The relation between nominal and real interest rate r is given by I + R = (I + r) * (I + p) . For small r * p , the expression can be reduced to R = r + p for simplicity.

3 Negative signs do not indicate negative effective tax rates (i.e. a subsidy). Rather, they are explained by a negative denominator in (2), which implies a negative tax base but, nevertheless, positive tax payments.

Sources: IMF International Financial Statistics; and own calculations.

As highlighted by the data in table 4, inflation distorts saving by discriminating against assets that are not inflation-proof. Tax policy, in principle, should acknowledge this problem by taxing real rather than nominal returns. Such attempts have rarely been undertaken in the past, mainly because systematic adjustments appear to be difficult and, hence, expensive (Sorensen, 1994, p. 63). Another attempt to account for inflation in the design of the tax system would be to apply reduced 20 tax rates on interest as compared to other types of capital income. However, this seems to be an inferior solution because the non-distorting tax differential depends on the actual inflation rate and would, accordingly, be subject to permanent change. In particular, moderate-to-high inflation economies like Hungary would face the need for constant adjustment.

Even though official statements mentioned that revenue considerations rather than ,,effective tax rate" considerations were the driving force behind the reform, and even though the tax reduction

**19** In principle, effective tax rates should be calculated using _expected_ inflation rates. Lacking better data,
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26
included other HEIs. Since the local HEI supply is likely to affect students’ enrollment choices, we use as instruments the availability of SC HEIs and other HEIs.

# **3 Data**

We use individual-level administrative data on student background characteristics, high school exit exam test scores, higher education enrollment choices and trajectories, and formal labor market outcomes for the universe of high school graduates in 2005. Individual records from the high school exit exam ( _Saber 11_ ) contain test scores for math, reading, science, physics, history, chemistry, geography, and philosophy, as well as student age, gender, and socioeconomic characteristics (e.g., mother’s education, household income level, and number of siblings) at the time of the test. Higher education enrollment, trajectories, and completion come from the System for Dropout Prevention of Higher Education (SPADIES, in Spanish).

The initial sample includes the universe of Saber 11 test-takers in 2005, restricted to those who were 14-24 years old (93 _._ 5% of the full set of test takers). We further restrict the sample to students with data on all socioeconomic variables and test scores. We match this sample with higher education enrollment data from SPADIES, and set an enrollment window of five years after high school graduation. We restrict graduation to take at least two and four years for those in SC programs and bachelor’s programs, respectively. In addition, we restrict graduation age to the 19-30 year-old range. We remove students who enrolled in more than four programs between 2005 and 2015 (less than one percent of students).

Wages and formal employment for higher education graduates are available in the Labor Market Observatory for Education (OLE, in Spanish) for 2008-2013.<sup>7</sup> For higher education graduates, we restrict entry age in the labor market to be at least 20 years. Our datasets do not include labor market information for students who did not enroll in higher education,
or inherit land, tradition obliges them to register it in the name of their husband or other male relative. This situation prevents women living in rural areas from starting/joining agricultural cooperatives<sup>8</sup> . It may also limit their opportunities for engaging in other types of entrepreneurial activities due to lack of collateral and inability to access bank loans.

Noticeable differences can be observed in the concentration of male and female entrepreneurs in nonagricultural activities. Self-employed women significantly outnumber men in trade, real estate and renting as well as in community, social and personal services while the number of men entrepreneurs is almost double that of women in manufacturing, construction and transport and communications (Annex 3).

The analysis of the Business Environment and Enterprise Survey (BEEPS) 2009 provides additional information on the experience of male and female-owned firms in the non-agricultural sector. The survey helps assess the performance and key obstacles to growth of SMEs and large firms in Serbia through interviews with the owners and top management of 388 companies in manufacturing and services sectors.

> 7 Babovic, Marija. 2008. _The position of Women on the Labor Market in Serbia._ UNDP and Gender Equality Council of the Government of  Serbia, Belgrade

> 8 Babovic, Marija. 2008. _The position of Women on the Labor Market in Serbia._ UNDP and Gender Equality Council of the Government of  Serbia, Belgrade

7
labor they supply. Tables 8 and 9 report OLS estimates in column (1) and two sets of IV estimates (i.e. _same sex_ and _two girls_ as instruments) in columns (2) and (3) for the sample of mothers and fathers, respectively.<sup>28</sup> All the specifications include the same set of control variables as in Table 7. The upper panel of these tables reports the estimates for the different measures of labor force participation (e.g. _worked in last 7 days, work off-farm, work on-farm, self-employed_ ), while the bottom panel reports estimates for _hours worked per week, second occupation_ and _monthly labor earnings_ .<sup>29</sup>

> 27The corresponding first-stage estimates for the sample of fathers presented in Panel B of Table 7 are almost exactly the same as the ones for the sample of mothers.

> 28The first-stage specifications of columns (4) and (6) in Table 7, which use _same sex_ and _two girls_ as instruments for fertility, are the ones selected for the second-stage estimation because these are the ones that proved to be the strongest instruments after controlling for covariates.

> 29The variables presented in the bottom panel of Tables 8 and 9 such as _hours worked per week, second occupation_ and _monthly labor income_ are only available in the Albanian LSMS surveys, but not in the Albanian DHS survey. For this reason, the sample of mothers is reduced to 4,862 observations and the sample of fathers to 4,530 observations. The first-stage estimates for these reduced samples are very similar to the ones shown in Table 7, but very slightly smaller in magnitude. Once again _two girls_ and _same sex_ prove to be valid instruments for fertility, while _Two boys_ seems to be a weak instrument. The first-stage estimates for these samples are reported in Table A-4 of the Appendix.

21
3,466 are from rural areas. The second round (ESS2) was collected in 2013/14. In this round, the survey was expanded to cover large urban centers by including an additional 100 EAs, and the number of sample households grew to 5,262, coming from all 11 regions of the country. Of these households, 3,323 are from rural areas. The urban sample households became nationally representative of all urban households of the country since this round, making the whole sample of households nationally representative of households from all areas of Ethiopia,<sup>12</sup> as the rural sample households have been nationally representative since ESS1. The attrition rate across these two rounds was about 5%. The final round (ESS3) was collected in 2015/16, successfully tracking and interviewing 4,954 of the sample households in the ESS2, implying that the attrition rate from the ESS2 was about 6%. Of these households, 3,272 resided in rural areas. One of the rural EAs was not visited in the ESS3 for security reasons.

The sample households were selected following a stratified, two-stage sampling design. The sample frames are the 2011/12 Agricultural Sample Survey of Ethiopia and the 2007 Population and Housing Census of Ethiopia (PHC) for rural and urban households, respectively. Each of the regions of Ethiopia served as a stratum. The first stage of the two-stage sampling process involved the selection of sample EAs, which is undertaken under the condition of drawing at least a prespecified number of EAs per stratum. The second stage involved selecting an equal number (15 from large urban EAs while 12 from others) of households from each sample EA, using a simple random sampling method (CSA and World Bank, 2015, 2017).

The ESS contains rich individual-level information including basic demographics (age, gender, and so forth), employment condition, and participation in labor-sharing arrangements. It further has household-level information including consumption expenditure, asset ownership, housing conditions,
land border with their country of origin. At the beginning of the sample 95 percent of refugees were in a country contiguous with their home country. That share fell to 77 percent in the the period 2012-2017.

Another manifestation of the increasing geographical reach of refugees is the greater number of destination countries to which they go. Similar to the global destination Herfindahl index above, we construct a source-specific Herfindahl index that captures whether refugees from a given source country diversify their destinations over time. That is, for a specific source country _s_ and year _t_ , the source Herfindahl index is 9
discretion. In practice, tax inspectors estimated businesses’ ability to pay based on their appearance and on discussion with business owners.

Data from our baseline survey (see Table 1) show that more than 70 percent of firms think it is difficult to know in advance how much taxes they will have to pay, with this being the case even for formal firms. Slightly more than half of the informal firms in our study (55 percent)

> 3 The OHADA General Commercial Law defines the _entreprenant_ as having an annual turnover below CFAF  30 million (USD 50,400) for trading activities, CFAF  20 million (USD 33,600) for crafting activities (artisans), and CFAF  10 million (USD 16,800) for services. Once the small business adopts the _entreprenant_ status, the turnover threshold should not be exceeded for more than two consecutive years.

> 4 In December 2014, the Beninese Parliament adopted a new MSE tax regime. This regime introduced the Synthetic Professional Tax (TPS: _Taxe Professionnelle Synthètique_ ) which replaces the four taxes that micro and small businesses were subject to before the reform. This new tax introduces a major shift by changing the basis of tax calculation from the rental value to the use of turnover. This reform creates more predictability and transparency in the calculation of the amount of tax due and prevents small businesses from abuses of tax officers. MSEs will start paying the TPS in 2017 based on their 2016 turnover. All _entreprenants_ will pay the TPS.

> 5 The four tax regimes were the following: “ _Taxe Professionnelle Unique_ ” (TPU), “ _Taxe Unique sur les Transports Routiers_ ” (TUTR), “ _Régime du forfait des revendeurs de tissus et divers_ ”, and “ _Régime du bénéfice réel simplifié_ ”.

6
6 times in Ethiopia, 3.6 times in Rwanda, 5.5 times in Ghana, and 7 times in Liberia.

Host country data do not distinguish private and government ownership. However, the differences are ostensibly caused by private projects assuming SOEs generally comply with MOC certification requirements. Also, the size and sector distribution of the projects reported by host governments, to be discussed below, support such a supposition. Further, field investigations in two countries, Ethiopia and Nigeria, also to be discussed shortly, confirm this.

> 14 Rwanda only has “committed” figures.

12
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BE indicators at the city-industry level as a proxy for the local BE (Dollar et al. 2005; Hallward-Dreimeier et al. 2006; Aterido et al., 2011; Xu 2011).

We look at several aspects of the business environments that are discussed earlier that might interact with female leadership in affecting productivity. Corruption is measured as Corruption Obstacle (i.e. the extent to which corruption is viewed as an obstacle by firms in the city-industry cell). 17 In particular, the survey asks, “to what extent is corruption an obstacle to the development of the firm?” and the answer is scaled from 0 to 4, where a higher number implies a more severe constraint. To aid interpretation, for this question, we construct a dummy variable of moderate or severe obstacles for that area (i.e., the values of 3 and 4), and call it Corruption Obstacles. We then calculate the city-average of Corruption Obstacles, and view it as the local BE indicator on corruption. To measure access to scarce and critical inputs that might need personal and professional networks, we have several indicators: Power Outage (i.e., city-industry share of firms that experienced a power outage in the survey year); Overdraft (i.e. city-industry share of firms with overdraft facility), which captures access to formal finance; and Trade Credit (i.e. city-industry average of the proportion of total annual sales of goods and services that are paid for after delivery). To capture the relaxation of dependence on personal network and the availability of modern technology, we use Web Intensity (i.e., city-industry share of firms that answer that they use websites to conduct business). To capture competition, we have Informal Competition, which is measured by the share of firms in a city that self-report as competing with informal firms.<sup>18</sup> We in addition control for other key measures of business environment such as _Capacity Agglomeration,_ which
both empirical and normative expectations – appear to correlate with women’s work status when work challenges gender traditional roles. This is also true for social expectations specifically around women working outside the home for pay, but it is not the case with most other dimensions of women in public places. Second, social sanctions, which are relatively low to begin with, are not correlated with women’s work status, with the exception of when households breach the male provider norm. For both men and women, higher expected severity of social sanctions on the male breadwinner role is associated with a lower probability of the woman working.

Overall, women's personal beliefs and social expectations are more significantly correlated with their work status as compared to their spouses’. For men, social expectations rarely correlate with their spouses’ work status, with the exception of empirical expectations about other women working for pay outside the home and working in mixed-gender environments. Their personal beliefs also correlate less with women’s work status and do not appear to matter when it comes to beliefs about appropriateness of women working outside the home and about men’s provider role.

When we analyze the individual level predictors of women’s work outcomes ( **Table 5** ), we find that for norms around household roles, personal beliefs, rather than social expectations tend to be more strongly correlated with women’s work status. Going from most liberal to most conservative in the PNB index for household roles is associated with an average 8 percentage points lower probability of women working for pay outside the home. In contrast, when it comes to norms around women in the public space (column 2) associations with personal beliefs and social normative expectations are not statistically significant. These results also hold when variables across both themes are considered in the same regression specification. For male respondents, however, even their own beliefs do not appear to influence employment outcomes of their spouses.<sup>29</sup> Overall, these findings suggest that
The company provides sales agents with a chat interface to interact with customers. Customers can initiate interactions with sales agents by clicking on a chat button at the bottom of the webpage. Clicking the chat button reveals a chat window displaying the agent’s first name and a short greeting message. Thus, agents always send the first message; either the agent or the customer can send subsequent messages as the conversation evolves.

The company was keen to partner with the research team to investigate whether they could optimize this chat/sales interface. This particular test aimed to identify how customer behavior changed with respect to agents’ identities—specifically when agents were assigned male- versus female-sounding names. To this end, the company needed to (1) randomize whether the name appearing in the chat implied a male or female identity and (2) ensure agents were unaware of their assigned names. The randomization was correctly implemented: a software program pulled one name per sales agent per day from an existing list. The agent then received the drawn name within the chat/sales interface. A local field team compiled the list of names by drawing 1,198 names from local school yearbooks and assigned each name an implied gender and ethnicity.<sup>12</sup> To limit the customers’ inference of other dimensions of agents’ identities besides gender, the interface only included agents’ (randomly assigned) first names.

Next, to ensure that agents could not see the names that were assigned to them, a web plugin was designed to omit the agent’s name from the agent-facing interface. The company installed the plugin on each agent’s internet browser with oversight from our field team. The plugin symbol was removed from the list of visible extensions—appearing as a light grey square when all browser extensions were listed. Agents did not inquire about the plugin throughout the duration of the experiment. The plugin worked in the following way. Consider a day when agent _James_ (real name) was assigned the name _Steve_