rafmacalaba/data-use-mentions-extended huggingface dataset:
add to the regressions controls for the educational attainments of woman’s mother and father.<sup>15</sup> This increase in the absolute value of the estimated coefficient suggests that education of parents is positively correlated with the residence in the more conflict affected area. Further (Col. 7) women from the warcohort in the more affected regions were about seven percentage points less likely to complete 11 grades of education than comparable women from the lesser affected regions. Again, the estimated coefficient on the interaction term increases in absolute value once I add to the regression a set of controls for the educational attainment of parents.

The coefficient on the stand-alone “war-cohort” dummy is negative and significant in regression for the sample of men with a dependent variable “Completed secondary schooling” (Col. 3) indicating that men who were of school age during the war were about 5.7 percentage points less likely to complete secondary school or above than men who were able to complete their schooling prior to the start of the war. The cohort dummy is also negative and significant in the regressions for the sample of women in Col. 7 and 8, indicating that women who were of school age during the conflict were seven (ethnicity controls only) or 12 (ethnicity and education of parents controls) percentage points less likely to complete secondary school as compared to women who were 18 years and older when the conflict started. No similar statistically significant effect is found on the education of men. In Table 1, Col. 3, the effect of being of school age during the war translates to a 5.7 percentage points (significant at 1% level) lower chances of men completing secondary school or more. However, this effect turns insignificant when controls for parental education are added to the regressions (Table 1, Col. 4).

> 15 I also used a specification where in the regressions with the 2007 data I used a dummy (“school closure” for schools in the raion being closed by
In 1980, Tanzania’s production was close to trend (hence the column label: Tanzania 0), while Uganda – a major source of maize imports into Tanzania – experienced a large negative production shock. Globally, many major maize producers and exporters experienced below-trend production in that same year. In contrast, Tanzania had below average production in 1982 and 1983. In 1982, major maize exporters had above-average production, putting downward pressure on prices. In contrast, in 1983, some of those major exporters had below-average production pushing up global maize prices to the advantage of smaller maize exporters that had good harvests in that year.

International maize trade in each of these scenarios could have had beneficial or detrimental impacts on Tanzania. However, an export ban on grains would have made it impossible for Tanzania to take advantage of higher world prices, especially in years like 1995 and 1971, when it had aboveaverage production while other African countries and major global traders had low production. Analysis of FAOSTAT maize trade shows that there were 14 years when Tanzania did not officially export any maize<sup>6</sup> . In six of these 14 years – 1976, 1977, 1981, 1986, 1996, and 1996 – Tanzania had a positive maize production deviation from trend. In some of these cases, the world price of maize was also above trend, suggesting that the country was missing an opportunity to take advantage of the higher commodity prices. While import restrictions in partner countries surely played a role in limiting Tanzania’s exports, Tanzania has had a range of different initiatives in place that have also contributed to low levels of exports<sup>7</sup> . For example, in 1995, when Tanzania had higher-than-usual maize production, the world price of maize was 38% above trend<sup>8</sup> . However, there was an export ban in place which limited the country’s ability to capitalize on this market development. This was finally lifted at the end of 1996
|**Male**|**Characteristics**|**Female**|**Male**|
|---|---|---|---|---|---|
|**Education**|||**Household Wealth**|||
|No education|34|1|Bottom Quintile|31|1|
|Primary|27|1|2nd Quintile|24|1|
|Secondary|17|1|3rd Quintile|23|1|
|Post-Secondary|11|1|4th Quintile|21|1|
|**Marital Status**|||Top Quintile|19|1|
|Not married|19|1|**Location**|||
|Married|36|1|Rural|28|1|
|**Employment Status**|||Urban|20|1|
|Not employed|25|1|All|24|1|
|Employed|13|1||||

Quantitative evidence from the MHYS further indicates that most young men and women hold the belief (‘prescription’) that women should perform more household tasks than men, as documented in Table 2. Women presently do more work inside the home and less work outside as compared to men. Interestingly, the "how would you like it to be" scenario is not much different across men and women. Further, while men are open to women working more outside of the house, they also want them to continue their housework routines. Women also reveal that they would like to engage in more of home production than men do, while simultaneously aspiring to work outside of the home. These responses reveal strong gender norms related to the labor market participation on the part of both men and 6
in support of the emergence of a domestic banking system. In light of the inverse relationship between moral hazard and banks’ capital, this deliberate policy exacerbated the banking crises and its inherent costs for Sub-Saharan African countries, however [Brownbridge (1998)].<sup>28</sup> Indeed, moral hazard was pervasive in the practice of banking in the nineties, and at such was singled out as one of the leading causes of a dramatic increase of nonperforming loans. In Nigeria for instance, moral hazard which fueled insider lending is believed to have accounted for over 65 percent of impaired loans in four banks liquidated in 1995. Similarly, most of large local banks failures in Kenya involved extensive insider lending [Bronwbridge (1998)].

This section investigates the determinants of nonperforming loans using correlation and causality analysis. The analysis is based on data drawn from 16 African countries (7 CFA and 9 non-CFA). The sub-panel of CFA countries includes: (1) Benin, (2) Cameroon, (3) Chad,

> 26 With the distressed financial system, interbank borrowings became the source of funds for most financial institutions. In Nigeria for instance, two of the failed Merchant banks had mobilized 84% and 68% of their total deposit liabilities from interbank deposits, respectively [Manu (1994)], a situation that led to rapid increase in interest rate. Real interest rates at over 20 percent were not uncommon for interbank borrowings.

> 27 Insider lending is not specific to Sub-Saharan Africa however; over the years, it has been singled out as the major cause of banks failure around the world [Caprio (1997)].

> 28 For instance, during that period, the minimum capital requirement was less than US$50,000 in smaller countries and no more than half a million dollars in larger economies such as Kenya and Nigeria. However, an increase in the capital requirement and banks capitalization was part of the restructuring measures. Over the past few years, capital requirements have
<!-- Start of picture text --> SD of TFP in country-industry groups<br>2<br>Co<br>0 1 2 3<br>x<br><!-- End of picture text --> <!-- Start of picture text --> SD of labor productivity in country-industry groups<br>2<br>o<br>0 5 1 15 2 25<br>x<br><!-- End of picture text --> <!-- Start of picture text --> SD of LPgrow in country-industry groups<br>o<br>N<br>o<br>0 5 1 15<br>x<br><!-- End of picture text -->
two weeks after the completion of the main survey, a follow-up survey was conducted with 1,000 respondents. The follow-up survey included selected questions from each of the themes from each social norms module. It was conducted over the phone with a random subset of respondents who agreed to share their phone numbers for the short follow-up survey. The purpose of this survey was to assess if responses to social norms and beliefs related questions stayed consistent over time.

# **Sample and Data Collection**

The main survey was conducted with a sample of 2,007 respondents from low to middle-income families, in urban and peri-urban areas of Amman, Zarqa, and Mafraq, between March and April 2018. Given the low rate of female labor force participation in Jordan, working women were oversampled at each location to obtain a sufficiently large sample size for comparison and analysis. Targeting at least 30% working women, the sampling methodology was a stratified cluster sample based off an enumeration exercise. With Amman comprising of 70% of the sampling frame, the governorate was under-sampled by 15 clusters, which were transferred to the Mafraq stratum. Localities within each governorate served as the primary sampling unit; grids within each locality as the secondary sampling unit; and within each randomly selected grid, census blocks acted as the tertiary sampling unit. Census blocks were enumerated to locate working women, and priority of selection was given to working women within each household. Lastly, a stratified (by work status and gender) simple random sample was drawn from eligible households (fourth sampling unit).

Quantitative data from the pre-test, quantitative survey, and follow-up survey were collected using tablets. Enumerators were trained extensively by research team members and the local data firm, and the survey was pre-tested to check for comprehension, proper administration, and survey fatigue. Enumeration data were used to pre-assign respondents to enumerators, and replacement households were also assigned to minimize enumerator discretion in the field. Data were analyzed using Stata to generate descriptive tables and figures and to conduct validity,
the country under investigation with available variables that resembles the index in the dataset for the pre-2019 periods as good as possible and then take changes beyond 2019 for country-specific growth analysis.

# **6.7 Creation of Growth Scenarios and Benchmarking**

A likely question that arises in a policy context is: what growth could a country expect if a certain explanatory variable _xk_ changes? Recall that the model is not suited to provide an estimate of causal effects for such a scenario, but rather a thought experiment along the lines: what income changes did countries usually experience during periods when _x_ changed, holding several other aspects constant?

Usually, benchmarking to an aspirational peer country provides a meaningful magnitude for a potential improvement in a growth correlate _xk_ . For example, column (3) of Table 3 shows that the infrastructure index for Bangladesh stands at 0.154 in period 8. From the data set in the GitHub repository, we can further infer that the infrastructure index in Indonesia stood at 0.655 in the same period. What growth did countries usually observe when they closed the remaining gap of 0.5 units? For simplicity, we assume that the closure of the whole gap happens in one period.<sup>27</sup> The model then implies an associated increase in income per capita levels of Δ _xk·β_ = 0.5∙0.08=4.1% in the short run and Δ _xk·β/(1-θ)_ = 0.04/(1-0.8)= 20.2% in the long run.

Such benchmarking exercises can help prioritize broad areas of reform because they combine partial growth correlations (captured in the parameters _β_ ) with magnitudes of gaps in typical growth correlates _xk_ . In other words, section 5.2 highlights how important _β_ possibly is across countries, but

> 27 This is a possible but very ambitious improvement. The average (median) change in the infra index per period over all sample observations is 0.
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- Glewwe, P. Q. Huang, and A. Park. 2017. “Cognitive skills, noncognitive skills, and school-to-work transitions in rural China.” _Journal of Economic Behavior & Organization_ 134: 141–164.

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), based on employment information in Costa Rica for 1980‐ 1992, showed that the minimum wage policy actually helped to create jobs in the country, mainly because of an increase in qualified employment. According to these authors, the industries that hired a highly qualified workforce were either facing a monopsonistic labor market or paying an efficiency wage that enabled them to increase their employment levels.

A more recent study by Alaniz et al. (2011) focused on the situation in Nicaragua, a country that has typically had high wages relative to the national average (a ratio of 0.53 for the mean minimum wage to the mean wage for 1998 and 2006 and a high median wage (a ratio of 0.81). Like several other Central American countries, Nicaragua has a system of multiple minimum wages for different sectors (14 sectors in all). The Alaniz study looked at variations in the minimum wage by state and by year to analyze the effects on employment. The authors used panel household data from 1998 through 2006 to show that increases in the sectoral minimum wage reduced the probability of being employed in the private sector. A 10 percent increase in the minimum wage reduced the probability of workers remaining employed in the private sector by 3.1 percentage points. However, this finding was only seen in large companies where there is greater compliance with minimum wage laws. The contraction in employment was the result of not only mass layoffs in the private sector, but also a reduction in the generation of new jobs in the private sector. In Peru, Céspedes (2005) also found evidence of adverse effects on employment for the period 1993‐2003, with an elasticity of −0.13 for the formal sector, which 9
parameter that is specified by the researcher. Common radial basis functions are thin‐plate spline and multiquadratic functions. See De Smith et al. (2007) for a comprehensive textbook that is reasonably accessible.

8 In a comparison of the performance of different interpolation techniques on rainfall data from May 8 1986 in Switzerland, the latter was found to be the most precise (Dubois, 2003). Yet, as highlighted by Dubois and Shilbi (2003), at 5.93 mm, the root mean square error (RMSE) for the multiquadratic function was only slightly
, prevented chatbots from sending automated follow-up messages. It went into effect one week after we began our study, which forced us to compress the timing and send the follow-up survey exactly one week after the study began, before the new policy prevented the follow-up from being sent. This disproportionately affected the documentary watchers, as they had more episodes to watch. While many were contacted manually later, they were still less likely to respond.

> 24The study’s initial power calculations estimated a two-sided test with power of 0.8, alpha of 0.05 and no intra-cluster correlation for the question “Do you think a husband is justified in hitting or beating his wife if he suspects her of being unfaithful” among 18-24 individuals living in urban India (NFHS-4). To detect a six-percentage point increase in this outcome, each treatment arm required around 500 observations. However, each follow up survey had approximately 200 observations per arm.

> 25The two panel subsamples tend to exhibit overall more progressive attitudes than the only-baseline subsample, and the differences are generally statistically significant. For instance, panel subsamples are less likely to justify domestic violence and more willing to report it. This is a consequence of this study trying to replicate the real world, where media consumption is an individual choice and tends to be biased. At the same time, it also suggests that program content matters and raises the potential concern that even the implicit format (a humorous fake reality TV show) could fail to attenuate self-selection into viewership by reaching and engaging individuals with different interests. On the other hand, the different subsamples are generally similar across socio-demographic characteristics, with the exception of female respondents, respondents with sisters and respondents with high media consumption who are all more likely to complete the two follow-ups.

13
this procedure results in higher pollution levels in large countries with known pollution problems in cities (notably China, Nepal, Pakistan) or those with forests (notably Lao PDR, Indonesia, Senegal), and in lower concentrations in areas with known deserts (notably Chad, Tunisia, Morocco).

<!-- Start of picture text --> Difference between pollution level in urban areas and country averages<br>20<br>15<br>10<br>5<br>0<br>−5<br>−10<br>Chad Tunisia Morocco Cote d'Ivoire Cameroon Ghana Guatemala Thailand EthiopiaBenin Swaziland Sri Lanka Zimbabwe TogoTurkeyGabon Botswana Solomon Islands Guinea MozambiqueFijiRwanda El Salvador Zambia Bolivia MadagascarNetherlands UgandaGermanySierra Leone Malawi NicaraguaCambodia Costa Rica Denmark Australia Venezuela, RBTanzania ParaguayDominican RepublicFinland Timor−Leste Congo, Rep.BelgiumNigeriaNamibia Panama NorwayIreland Colombia KenyaEcuador PhilippinesLesotho JapanSpainHonduras Sweden Brazil BangladeshFrance MongoliaSwitzerland Vietnam Suriname Iran, Islamic Rep.Greece United KingdomGuinea−Bissau Austria UruguayAzerbaijanUnited States Central African RepublicCanada Indonesia ArgentinaItalyMexico Uzbekistan MalaysiaSenegalAngolaSouth Africa GeorgiaLao PDR Peru Chile India Armenia TajikistanKazakhstan Pakistan NepalChina<br><!-- End of picture text --> Figure 1: Difference between average pollution in urban areas and country-wide average PM2 _._ 5 data.

# **3.3 Vegetation control data**

We use the NDVI from the Moderate resolution Imaging Spectroradiometer (MODIS) from NASA’s Terra satellite to control for effects that relate to a variety of physical characteristics and natural assets of a country.<sup>11</sup> This dataset provides spatial and temporal comparisons of global vegetation conditions. The original data have a monthly frequency at a resolution of 1km. We calculated the mean NDVI value for each year in our analysis, we use 20002015 data, and summarized the data to the country-level using the mean, minimum, and maximum value to get a broad description of the vegetation in a country.

# **3.4 Other data**

For our economic variables and data on carbon dioxide emissions, we
and Devereux (2011), Bjorklund and Salvanes (2011), Solon (1999), Heckman and Mosso (2014), Mogstad and Torsvik (2021). For surveys of the sociology literature see Breen (2010) and Torche (2015a).

> 3The focus on geography of intergenerational mobility also reects the inuential work of Chetty et al. (2014) which constructed an opportunity atlas with estimates of absolute mobility at the zip code level in the United States.

> 4For example, German President Joachim Gauck declared equality of opportunity as the normative policy goal during his inauguration in 2012. US president Barack Obama in his 2014 state of the union address mentioned opportunity 10 times.

> 5It has been increasingly emphasized in the literature that intergenerational mobility and inequality of opportunity are closely related, and they focus on the same fundamental question, even though these two strands of the literature developed independently. For discussions, see Bjorklund and Jantti (2020) and Emran and Shilpi (2021).

> 6There are other diculties in measuring household and individual income in a developing country. First, because of large informal home-based economic activities, it is dicult to measure income. Second, It is dicult to measure individual income in an extended family living and eating together in the same household. For a discussion on this point, see Iversen et al. (2019).

1