rafmacalaba/data-use-mentions-extended huggingface dataset:
These findings suggest that conflict has important consequences for the structure of economic activity. The impact of a surge in conflict-related casualties is much stronger on formal and illicit activities than on overall economic activity. This is shown by Figure 3, which traces the evolution of aggregate, formal and illicit economic activity based on the estimated coefficients in our preferred specifications and the assumed 0.25 elasticity of aggregate economic activity to nighttime light intensity. Both panels in this figure are computed for an increase in conflict-related casualties by one standard deviation. In panel 3a the shock lasts for one quarter only, whereas in panel 3b it continues over time. This figure also shows the predicted impact on informal economic activity based on equation (6) and the estimated sectoral weights in total economic activity.

# **Conclusion**

Most available estimates of the impact of conflict on economic activity focus on indicators giving a disproportionate weight to the formal sector. And this is despite informal and illicit activities being common, if not predominant, in conflict settings. Aggregate indicators such as GDP tend to underestimate informal sector activities, and to exclude illicit activities. Wages matter only for relatively better-off segments of the population, as the majority of those at work tend to be farmers or self-employed. Disaggregated indicators from non-traditional sources, from housing prices to construction permits to mobile phone traffic from registered firms, also refer to formal markets and organizations.

While some studies have analyzed the effects of conflict on informal and illicit activities, this paper is the first one to consider impacts across formal, informal and illicit activities simultaneously. The proposed methodology is firmly anchored in the literature, although it pays special attention to the dynamic structure of conflict impacts, and it controls for other variables that have been shown to affect economic activity in conflict settings. The main innovation of the paper comes from the reliance on non-traditional sources of data with a high degree of spatial granularity. Thus, nighttime light intensity is used to measure economic activity at the aggregate level, mobile phone traffic from registered firms
attendance costs, instructional expenditure, and percentage of students in each type of major. We match this data set with the six university rankings. Among U.S. higher education institutions, 294 are present in at least one of the rankings, and 108 are present in all four.

The simple OLS regression estimated is the following: 2014 ) ) p (8) bb,aa,ee aa aa ee 1,aa bb,aa,ee 2,aa bb 3,aa ee bb,aa,ee where the dependent variable is the annual average log earnings in 2014 of the cohort born in year log (eeT aaTTaae = αα + ββ QQ + γγ log (ppeeT aaTTaae + γγ TTe + γγ TT_SSTTEESS + εε _b_ of gender _g_ that went to college _c_ . The main regressor of interest is _Q_ , the quality measure based on the six rankings for college _c_ . Coefficient _β_ is the productivity effect of quality; it is used as the quality-adjustment factor in equation (7) which feeds into the ECA-HCI. Other regressors are the log parental earnings of the cohort born in year _b_ of gender _g_ that went to college _c_ when the individuals were 15–19; the age of cohort _b_ in 2014; and percentage of STEM majors in college _c_ in year 2000 (included to control for the STEM wage premium). Standard errors are clustered at the college level.

Table 4 provides the results for the aggregate quality score derived from the combination of the six rankings, shown for the sample of universities that are present in at least one of the rankings (323 universities in total) and for the common sample of 98 universities that are present in all the rankings. Table 5 summarizes the values of _β_ and _m_ (the implied productivity of a “zero-quality” institution) that arise from the results of the OLS estimations of equation (8), focusing only on values that refer to both genders. Full results are available in Table A.1 in the appendix.

**
younger girl in HH enrolled in secondary school*program|||0.08<br>(0.06)|
||||34]|
|N|4,718|4,718|4,718|
|R-squared|0.12|0.12|0.14|
|programspillovereffect(oldergirlinHH)||0.04|=—-0.10***|
|||(0.04)|(0.04)|
|||[64]|[.00]|
|programspillovereffect(youngergirlinHH)||0.02|0.10|
|||(0.03)|(0.06)|
|||[42]|[.13]|
|meanoutcome|0.60|0.60|0.60|

Sample is boys of secondary school age (13-18), Regions 2-6, Gambia Integrated Household Survey 1992/1998/2003/2010. “Older” girls include those of same age as boy. Program spillover effect is sum of program and program’ girl in HH coefficients. Standard errors in parenthesis, clustered by region. p-values in brackets, calculated from 200 iterations of wild-t cluster bootstrap of Cameron et al (2008) with Webb (2013) 6-point distribution of weights. Starred significance levels are based on these p-values. * significant at 10%; ** significant at 5%; *** significant at 1%.
ecological, economic and political environment. Year dummies were included to control for the survey year.

The disaster data were mapped into the household survey data at the commune level, which are on average only 30.3 kilometer squared.<sup>35</sup> More precisely, the centroid for each commune was identified and given the highly covariate character of the different disasters, the state of nature observed at that point in the disaster maps was assumed to be the state of nature experienced by all the VHLLS households in that commune. It emerges that while most households have some limited drought risk, twenty percent of the population has at least 30 percent chance of being flooded each year (Chart 1). At the same time, about two fifths of the population will never be affected by a flood and storms pass by about half the population.

Given the concentration of flood events, it does not come as a surprise that there is a strong intertemporal correlation in the occurrence of floods (0.44 for flash floods and 0.55 for riverine floods), i.e. households experiencing a flood his year also have a high chance of experiencing one the following year (Table A2). As expected, there is also a correlation between the statistical frequency 32 Two sources of water are typically distinguished in Vietnam: water for direct human consumption (drinking, cooking), and water for sanitation and maintenance purposes (washing clothes and bathing). Water is considered clean if it comes from (1) private tap water inside the house, (2) private tap water outside the house, (3) public tab water, (4) water pumped from deep drill wells, (5) water from hand‐dug and reinforced wells, (6) rain water, (7) purchased water, or (8) water from a water tank.

33 Sanitary latrines include flush toilets with septic tank/sewage pipes, suilabh toilets, and double vault compost.

34 The VMAP0 (NIMA
important lesson is that the econometric evidence on the relevance of ownership suggests that in general, there is no statistically significant difference between the efficiency performance of public and private operators in this sector. The only paper on Asia (Estache and Rossi (2002) and the paper using the latest data on Africa (Parker and Saal (2004) are both quite robust at that level. An earlier econometric study on the efficiency of the African companies found, with a much smaller sample, some superiority for private operators but the efficiency effect of ownership was much lower than the effect of good governance in the sector. This mixed review of the efficiency payoff of private sector participation may have to be correlated with the observation that water is the sector with the highest rate of renegotiation. According to Guasch (2004), 75% of the water contracts signed in Latin America during the 1990s were renegotiated.

The story is however not that simple. Indeed, the first published country specific paper on this issue in this sector shows the high efficiency payoffs of the privatization policy for 4 Argentinean provinces based on the calculation of a Malmquist index (Estache and Trujillo (2003)). Annual efficiency gains vary from 2.3% for the largest concession to 13.5% for one of the smaller ones. Note that the first average is over an 8 year period and the second over a 3 year period. The difference is at least partially explained by the fact that the biggest bang for the buck of privatization in these industries is immediately after the reform since the operators know that they will eventually have to share with the users the efficiency gains as part of a scheduled tariff revision. This is in fact illustrated by Seroa da Motta and Moreira (2004) in their recent working paper when they document that private operators stimulate a quick catching up but don’t get associated to frontier shifts more than public operators in this sector in Brazil.

The second lesson to emerge is that there is a clear case for increased yardstick competition in
regional budget, but few regions do so).</u> **<u>Maternity capital</u>** This is a lump-sum payment (453,026 rubles) granted to each woman who gave birth or adopted the second (third and consequent) child. Each mother is entitled irrespectively of the date of a birth of the first child, but only once. Mothers are 34 However, in 2013-14 the coefficient was higher than inflation, while in 2015-16, due to fiscal consolidation, the amounts were “frozen” for the majority of benefits.

35 The average ruble exchange rate was 60.9 rubles per U.S. dollar in 2015 (RLMS survey year), and 58.7 rubles per U.S. dollar in 2017Q1 (RUSMOD calibration). The 2011 PPP conversion factor is 23.4.

36
5 and the key features of the neoclassical growth literature. The model is then briefly described, highlighting in particular the importance of the underlying policy framework in determining 2 economic growth. The following section discusses our empirical results . Section V presents some convergence and growth exercises, with special emphasis on the implications of different policy and non-policy scenarios. We conclude by discussing the main policy implications that can be derived for CEE countries and their accession quest. This paper will build on the findings of the recent econometric studies of the growth literature (e.g., Barro, 1991) and on the convergence discussions for CEE countries initiated by Sachs and Warner (1996).

# **II. Convergence of Incomes across Countries**

First, some definitions. In neoclassical growth models a country's per capita growth rate is inversely related to its starting level of per capita income. This is known in the growth literature as the _absolute convergence_ hypothesis, which hinges on the existence of diminishing returns to reproducible capital: _given sufficient time all countries should converge to similar per capita_ 3 _incomes ._ The hypothesis of absolute convergence implies that rich countries should grow at a slower rate than poor countries. However, this is not consistent with cross-country data. As shown in Figure 1, the growth rates per capita (defined in this paper in terms of persons in the labor force) show little correlation with the starting levels of GDP per capita in a sample of over 80 countries for the period 1965-89. Other authors, working with different data sets, have also identified similar patterns (see, for example, Barro, 1991). This contrast between the empirical evidence and the 2 The more formal presentation of the model is found in Appendix I and is followed in Appendix 2 by a thorough discussion of the estimation results. 3 Some growth models treat savings as exogenous (e.g., Solow, 1956) while others have aimed at endogeneizing the savings decision process by introducing an optimizing representative consumer. The latter group
The focus in economic models on years of schooling is easy to comprehend. Years of schooling is an easily measured metric; educational quality and “skills” on the other hand is a more amorphous attribute. Focusing on education quality rather than years of schooling has substantially complicated economic analysis. In recent years, however, internationally comparable student achievement tests such as the _Trends in International Mathematics and Science Study (TIMSS)_ and _Program for International Student Achievement (PISA)_ have become more widespread. Their growing coverage has led to increasingly reliable indicators of educational quality.

Cross-country scores on TIMSS and PISA provide one indicator of skills, However, the skills measured by such tests are primarily cognitive skills. Such skills are clearly valuable in technologically advanced economies. In developing countries, however, the situation is different. The requisite skills and returns to education in developing countries are markedly different due to a less advanced technological base and fundamentally different economic structures. Thus, cognitive skills as measured by international student achievement tests may not play as fundamental a role in human capital enhancement in developing countries.

The extant literature on human capital and economic growth has to a large extent focused on developed countries since data is more readily available for such countries. International student testing has been extended to all OECD countries and comparable data for OECD countries are now readily available (see Hanushek and Woessmann (2012)). However, research on the relationship between human capital, skills and economic growth in developing countries is considerably sparse since comparable test score data for such countries is not available. This may not, however, constitute a major disadvantage since the notion of skills in developing countries 3
method is preferable to selecting an aggregate approach, as has been carried out in previous policy assessments (International Telecommunication Union, 2020). Nightlight luminosity is measured in ‘Digital Numbers’ (DN) and the mean luminosity is used to allocate high, medium, and low consumption tiers for broadband spending (>5 DN, <5 DN and <1 DN respectively). ARPU estimates for these consumption tiers are then adapted for Côte d’Ivoire, Mali, Senegal and Kenya (high: $8, medium: $6, low: $2), and for Tanzania and Uganda (high: $8, medium: $3, low: $2), using GSMA data as the median value (GSMA, 2020), and then low and high values selected based on consumer price research for different mobile services. Revenue is converted over the 10-year assessment period to the present value (PV) in the initial year using a discount rate of 5% to represent the time value of money due to inflation, with this value informed by IMF consumer price projections (International Monetary Fund, 2021).

# 3.3. High-resolution spatial estimation of least-cost networks

The network design module estimates the least-cost design to connect communities without cellular coverage. Firstly, a baseline is established for existing infrastructure using a range of data sources. Long distance fiber links are extracted from the African Terrestrial Fiber map (Network Startup Resource Center, 2020), and fiber Points of Presence (POPs) are estimated based on large settlements exceeding 20,000 inhabitants located within 5 km of a fiber edge. Secondly, existing sites in each region are estimated to obtain the total existing site density using either geolocated site data, or disaggregated estimates of tower counts by country, as detailed in Section S1 of the Supplementary Materials.

A standard way to dimension a cellular network is using three cells per site, thus producing geometric hexagons (Holma and Toskala, 2012). Of the total existing site density in each area,
is a powerful predictor of final outcomes. This point is further illustrated in panel b, which classifies students into tiers by the end of year 1. Consistent with panel a, it shows the strong predictive power of early performance—as explained by the next stylized fact.

**Figure 4: First-year Classes Completed and College Outcomes.**

<!-- Start of picture text --> 23 60<br>21<br>19 45<br>17<br>15 30<br>13<br>15<br>11<br>9<br>1 2 3 4 5 0<br>Ability quintile Tier 1 Tier 2 Tier 3 Tier 4<br>On−time graduate Late graduate On−time graduate Late graduate<br>Dropout after 1st year Dropout 1st year Dropout 1st year Dropout 2nd year<br>All students Dropout after 2nd year<br>a. First-year classes completed b. College outcomes by first-year<br>by college outcome tier of classes completed<br>completed Percent<br>Avg. number of classes<br><!-- End of picture text --> _Source_ : Calculations based on SPADIES for students from the 2006 entry cohort (first semester). _Notes_ : In panel a, each color represents a college outcome. The green line, for instance, shows average number of classes completed by the end of year 1 by students of a given ability quintile who went on to graduate on time. The thick black line does the same for all students regardless of college outcome. In panel b, the graph shows the percent of students from a given performance tier by the end of year 1 that attain each college outcome. For the first year, tier 1 corresponds to 19+ classes completed; tier 2 to [17, 19); tier 3 to [13, 17); and tier 4 to [0, 13).

# _Fact 4: Cumulative performance is highly
reduced risk of T2DM and hypertension, although there were very small numbers of LMIC studies included in these estimates (Chen et al. 2022; Feng et al. 2022; Mishali et al. 2019; Khoramdad et al. 2017; D. Gao et al. 2013).

In contrast, high consumption of unhealthy foods and beverages, including UPF and SSB was associated with increased risk of T2DM and hypertension. Relative risks for diabetes incidence associated with SSB intake ranged from 1.27 to 1.38 (Neelakantan et al. 2021; B. Li et al. 2023). Adherence to unhealthy dietary patten also increased the risk of T2DM (Alhazmi et al. 2014; Maghsoudi, Ghiasvand, and Salehi-Abargouei 2016; McEvoy et al. 2014). UPF consumption significantly increased the risk of hypertension (OR 1.23; 1.11, 1.37) (M. Wang et al. 2022).

# Evidence gaps by life stage group, dietary exposures and outcomes

In **Table 8** we show a mapping of the associations observed in the included SRM by life stage group, dietary exposures and outcomes.

**Diet quality during pregnancy** – the evidence available on the risks associated with various dietary factors during pregnancy covers a wide range of maternal nutrition and health outcomes, as well as birth outcomes (e.g. LBW). Diet diversity is a common metric used to assess nutrient adequacy during pregnancy, particularly in LMICs, but is less useful for estimating the risk of pregnancy complications such as gestational diabetes and hypertension. Dietary risk factors for NCD-related outcomes were commonly assessed using specific food groups as well as

11
ment. The rate in the female-assigned sample is practically identical to the male-assigned sample and does not differ statistically.

Finally, column (3) of Table 3 tests whether customers bargain more often with female sales agents. While 15% of chats exhibit some bargaining behavior—for example, asking for discounts on the listed price—we find no significant effect of female-name assignment on the likelihood of bargaining. This null result is fairly precise relative to the baseline level and rules out changes to bargaining that exceed 20%. Therefore, in this context, differential bargaining does not appear to drive the observed productivity differences.

Together, our results suggest that customers interact differently with women and men in ways that can meaningfully reduce productivity. This result is especially consequential for the service industry, where customer-facing roles abound. Our investigation of the mechanisms behind this behavior suggests that consumers engage less with female agents—along extensive (any engagement) and intensive (tone used) margins. We find no evidence that customers differentially harass or bargain with women in our setting.<sup>26</sup>

# **4.3 Comparison to non-experimental estimates**

We compare the results from our experimental research design to a simpler non-experimental comparison of male and female agents. The non-experimental results measure correlations between chat purchases and agents’ actual gender using chats _outside_ the experimental sample.<sup>27</sup> We include similar controls in both specifications but cannot include agent fixed effects in the non-experimental comparison as they are collinear with gender. Table 4 shows correlations between agents’ actual gender and sales in Panel A and experimental estimates of female name assignment in Panel B. Only administrative sales records are available for the non-experimental sample, so we compare across samples using these outcomes. In Panel A, we find no statistically significant differences between male and female agents across any of the three purchase outcomes. In Panel B, we find female name assignment leads to statistically significant reductions in sales across all of the administrativerecord purchase outcomes.<
i) survey-based methods, ii) imputation-based methods, iii) estimation-based methods.

I start with direct measurement from surveys.

# **3 Survey-based Methods**

The best method to measure a phenomenon is to collect direct quantitative data about it. This goes for transportation costs as well. Since a survey can target its sample across different types of firms and entities, it is also the only avenue to potentially obtain information on the price of road transport paid by end users separate from its

> 2Wilson (1987) models and estimates the price structure using trucking data between a limited number of regions within the U.S., documenting the prevalence of backhaul problem. This same effect has been documented for international maritime shipping by Wong (2022).

6
efforts are frequently obstructed. In this context, recent studies (Turner et al., 2015; Levin et al., 2019; Mobaied and Rudant 2019; Garzón and Valánszki 2020; Aung 2021) have emphasized the potential importance of remote sensing from satellite platforms. These technologies offer a promising solution by providing valuable and consistently collected real-time data from areas that may otherwise be inaccessible or where traditional monitoring is too challenging.

The Global Biodiversity Information Facility (GBIF) is also playing a critical role in overcoming these barriers. By acquiring and distributing data on species sightings from concerned organizations and individuals, the GBIF is providing essential information in areas where officiallysupported monitoring may be sparse or difficult to implement. This collaboration strengthens global conservation efforts by making vital data more accessible, even in fragile or conflictaffected regions.

To implement effective conservation strategies in such contexts, accurate, location-specific data are crucial. The importance of this data for identifying critical biodiversity areas and tracking 3