rafmacalaba/data-use-mentions-extended huggingface dataset:
enumeration areas (EAs), 580 EAs in GLSS 5, and 300 EAs in GLSS 4.<sup>8</sup> Based on the location information, we match households to districts of regions with oil and non-oil regions. The GLSS 5 data includes 110 districts while GLSS 6 includes 170 districts.<sup>9</sup> The number of districts in Ghana has increased over the years from 110 districts in 2000 to 170 districts in 2010. With information on both the original districts that have been split and newly created districts, we are able to match the new districts back to the old districts that existed in 2000.

Data on household income is taken from the Rural Income Generating Activities project (RIGA), which is compiled by the Food and Agriculture Organization of The United Nations.<sup>10</sup> The RIGA income data is constructed using household survey data (GLSS), with the idea of including all sources of household income that are often neglected in the computation of income. Such sources include income from selfemployment and income from own-farm production of crops. Additionally, we use information on the location of oil fields from Petroleum Commission of Ghana to determine the oil and non-oil regions. The data contains information on the number of offshore oil fields, wells, location, year of discovery and status of operation, which is whether the oil field is currently producing, under appraisal or exploration.

The empirical analysis focuses on regions in Southern Ghana by comparing households and individuals in treatment and control areas. We focus on the southern part of Ghana for two reasons. First, we limit our focus on offshore oil. The oil discovered in 2007 as well as the subsequent discoveries are offshore. We define treatment areas as districts in regions that face offshore oil or districts in regions that have oil in its offshore boundaries. Second, districts considered in our analysis are more similar to each other compared to districts in the north. One of the regions in the south that has oil
Bank (2011). For the thermal benchmark, data represent primarily residential users. _Note:_ Access to electricity data for urban and rural access in South Sudan are estimates based on calculations from the AICD economic model for power investment needs. Installed capacity per million people was calculated based on Platts (2009). * The aggregate is based on manufacturing firms.

South Sudan also records very limited usage of modern fuels for domestic consumption purposes across all income levels; in fact, usage is among the lowest in Africa. Recent poverty assessments for South Sudan found that less than 1 percent of the richest household quintile used modern fuels for cooking. Around 70 percent of the wealthiest household quintile and around 86 percent of the country on average used firewood for cooking (World Bank 2010a and 2010b). Comparing South Sudan to other countries in Africa indicates that the country records extremely limited usage of modern fuels overall (table 12).

**Table 12. Even the richest households in South Sudan do not use modern fuels for cooking**

|Usage of modern fuels (%)||
|---|---|
|All|Richest|
|<br>households|<br>households|
|Sudan (2009)<br>36.1|64.9|
|Kenya (2003)<br>3.45|16.52|
|Nigeria (2003)<br>1.19|5.51|
|Uganda (2001)<br>0.87|4.34|
|Ethiopia (2005)<br>0.31|1.48|
|**South Sudan (2009)**<br>**0.3**|**0.8**|
|Tanzania (2005)<br>0.24|1.22|
|_Source:_DHS various years and World Bank<br>World Bank 2010b.|2010a and|

Modern fuel usage for other purposes such
little evidence of increased subcontracting (Sanchez, Joo and Zappala 2001) in Mexico that would suggest a large subcontracting out of formerly formal jobs for competitiveness purposes.

**_Wages have probably benefited over the medium term:_** Conditional wages are, in fact, higher in industries more exposed to trade. IADB (2004) documents numerous studies suggesting employment declines during the adjustment process, particularly in industries with substantial rents, and among specific demographic groups  but assert that there is no empirical basis for a permanent decline. The widening skill earnings gap discussed below is observed in all countries that liberalized their economies, but seems less directly related to trade _per se_ than to greater skill biased technology flows common around the world.

**_The emerging jobs seem desirable._** Though sample sizes are small, surveys of _maquila_ workers suggest these jobs are as attractive (see for example, Sargent and Matthews 1999) or more so than alternatives and Feenstra and Hanson report (1996) that such firms tend to demand more in terms of skills than other industries.  Employees in the flower industry in Ecuador and the fruit industry in Chile earn more and have more stable incomes than those in traditional industries (de Ferranti, et al 2002)in the agricultural sector. Sanchez, Joo and Zappala (2001) argue that micro firms that are more closely linked to large (and hence more likely to be tradeables producing) firms earn premia of around 30 percent on non-linked micro firms.

**_Worker risk_** Despite concerns that trade liberalization would lead to greater worker uncertainty (Rodrik 1997), at present there is little evidence of it. Most recently, IADB (2004) finds no evidence of reallocation between industrial subsectors and Wacziarg (2001), using a global panel including LAC, finds only weak and borderline significant effects.  One key transmission mechanism, an increased wage elasticity of labor driven by higher product elasticities, has not materialized in Colombia or Chile and shows only fragile appearance in Mexico (Fajnzylber and Maloney,
higher protective effect among adolescents 10-19 years (OR 0.49; 95% CI 0.38, 0.63) compared to adults 20+ years (OR 0.77; 95% CI 0.66, 0.90) (Horta and de Lima 2019).

Based on cross-sectional studies, total dairy product (including milk, yogurt and cheese) consumption in children 2- 21 years is associated with a reduced risk of obesity (OR 0.66; 95% CI 0.48, 0.91) but not overweight (Babio et al. 2022). In a meta-analysis of 14 studies (11 cross-sectional and 3 cohort), regular consumption of _whole_ milk (compared to reduced fat milk) was associated with a lower risk of overweight and obesity among children 1-18 years (OR 0.61; 95% CI 0.52, 0.72) (Vanderhout et al. 2020).

High SSB intake is also associated with increased BMI in children 2-18 years, with a mean difference of 0.75 kg/m2 (95% CI 0.35, 1.15), higher waist circumference (WMD 2.35 cm; 95% CI, 1.34, 3.37; p = 0.016) and higher body fat percentage (WMD: 2.81; 95% CI 2.21–3.41; p < 0.001) (Abbasalizad Farhangi et al. 2022).

# Children 5+ years of age

Our search identified 16 SRM on the risk of malnutrition associated with dietary intake in older children, with four overlapping with children <5 years and one overlapping with adults (see **Table 6** ). As with children <5 years, there is evidence that low diet diversity among children 5-18 years across six LMICs is associated with increased risk of stunting (OR 1.43; 95% CI 1.08, 1.89), and wasting (OR 2.18
rule of law lies on the supply side—for instance, the inability to collect taxes to finance a market-oriented system (Johnson, Kaufmann, and Shleifer (1997) and Roland and Verdier (1999)).  But financial problems reflect endogenous decisions of essentially the same kind as the demand side that we analyze here.  In Russia, it was clear that the state was giving away assets of value an order of magnitude greater than the cost of administering a rule of law system.  The lack of finance was a reflection of the same lack of demand for the rule of law on which we focus here.

3 The governance indicators are weighted composite scores constructed by Kaufmann, Kraay, and Mastruzzi (2005) from indicators produced by 31organizations, including Freedom House and the European Bank for Reconstruction and Development.

2
column 1, this translates into a 1.7% fall in productivity or earning potential.<sup>8</sup>

# 5. Robustness Checks

We carry out several robustness exercises to further corroborate our baseline results.

In order to examine the possibility that these results are driven by spurious spatial or temporal patterns, the analysis is subjected to falsification tests. The first test involves re‐estimating equation (1) while replacing each individual’s exposure condition with exposures that occur for 6 different four‐year periods before or after birth up to age 9. The resulting coefficient estimates are plotted in Figure 3 against the different window periods of exposure. All the

> 7 Baum et al. (2007) and Bazzi and Clemens (2013) provide explanations of these tests.

> 8 As a robustness check, we also make use of the Indian Human Development Survey (IHDS) to measure the  impact of early-life exposure to nitrogen pollution on later-life wages using a similar methodology described in Section 2. The IHDS is a nationally representative survey. The survey provided a more complete recording of men’s earnings that we use as our main variable of interest. In unreported results, we find that full exposure in early-life decreases wages in adult life by 9%, on average, providing direct proof of the impact on labor productivity.

11
# **4. Results**

Table 4 presents the estimated coefficients from the hazard term of the full Probit model that includes all the natural hazards, controls for regional fixed effects, and standard errors clustered within administrative units. It also displays the estimated average marginal effects revealing heterogeneous effects among natural hazards across countries. In the case of Brazil, both floods and droughts present a direct correlation with household poverty. Conversely, earthquakes, cyclones, and landslides positively correlate with poor households in the Dominican Republic. Poverty in Ecuador and Peru correlates positively with the number of landslides and in El Salvador with the number of droughts, earthquakes, and cyclones. In Honduras, the hazards that positively relate to poverty are earthquakes and droughts. Finally, cyclones and floods are positively associated with household poverty in Mexico.

Moreover, the correlations presented in Table 4 lead to some interesting conjectures. For example, when both ThinkHazard! and EM-DAT coefficients are positive, such as cyclones in Mexico, the most common hazards coincide with the most disruptive hazards regarding poverty status suggesting a double high association. But, in some cases, significant estimations show opposite signs in hazard variables, e.g., floods in Brazil show a positive correlation with the ThinkHazard! index and a negative correlation with the EM-DAT variable. ThinkHazard! is based on the likelihood of each region experiencing a hazard with enough intensity to cause damage. Meanwhile, the EM-DAT measures provide the frequency of past events. When the coefficient of ThinkHazard! is positive and the coefficient of EMDAT is negative, it suggests, on the one hand, that hazards with more intensity of causing damage are more likely to have a greater impact on communities than expected. On the other hand, more frequency (controlling already for intensity) could imply that communities that have experienced these events in the past could develop some forms to prevent and adapt between occurrences, preventing a continuous cycle of poverty. Conversely, when the coefficient of ThinkHazard! is negative and the coefficient of EM-DAT is positive,
Further analysis shows that this failure to properly frame the questions about work and inactivity primarily affected women in 1992/93. Nine percent of females in a household reporting any agricultural activity at all provided the inactivity response “domestic duties”, but only 0.3 percent of men provided this response (Table 4). 72 percent of females of working age in these households who provided this response (and hence would be coded as inactive), reported a secondary economic activity. Nearly half of the males reporting domestic duties did the same thing. Unfortunately, most analysts (statistical offices and researchers) tend to only look at the response for the “primary” or “main” economic activity when creating aggregate employment indicators such as LFPR or distribution of employment by sector. Even for unit record data analysis, many analysts do not look at the secondary activity at all. This means that all the respondents who were actually economically active, but considered such economic activities as secondary to other non-economic activities they perform (e.g. going to school or caring for members of the household), would be misclassified as inactive and out of the labor force. If they are not counted, the LFPR would be understated by at least 8 percentage points in 1992/93.

**Table 4: Reports of non-domestic secondary activities among individuals whose main activities are domestic activity, 1992/93**

|**Description**|**Agric**|**ulture househ**|**old**|
|---|---|---|---|
||**Male**|**Female**|**Total**|
|**_Inactive - Primarily activity is domestic work_**|0.7|9.2|5.1|
|**_Share of above who report non domestic secondary activity_**||||
|No|51.7|27.5|28
9 In practice, we will measure the ratio λ at<sup>/λ</sup> bt using exports of countries to the United States. While it would be preferable to use their worldwide exports, our data for the U.S. are more disaggregate, and allows for a finer measurement of “unique” products sold by one country and not another. Specifically, for 1972 – 1988 we will use the 7-digit Tariff Schedule of the U.S. Annotated (TSUSA) classification of U.S. imports, and for 1989 – 1997 we shall use the 10-digit Harmonized System (HS) classification of imports.

To measure the ratio λ at<sup>/λ</sup> bt , we need to decide on a consistent “comparison country.” For this purpose, we shall use the _worldwide exports_ from all countries to the U.S. as the

* C c comparison.  Denote this comparison country by *, so that the set I t = Uc = 1 I t is the complete

* * set of varieties imported by the United States in year t, and pit<sup>q</sup> it is the total value of imports for good i.  Then comparing country c to country * in year t, it is immediate that the common set of c * c goods exported is I t ∩ I t = I t , or simply the set of goods exported by country c.  Therefore, from c (7) we have that λ t I(<sup>c</sup> t ) = 1 , and:

Noting from (8) that product variety in country c relative to the comparison is measured c c * c as λ t I( t /) λ t I( t ) , but this has a negative coefficient when σ < 0, let us instead invert it and

* c c c * c measure product variety of country c relative to the world by λ t I( t /
opportunities for informal childcare rather than community-specific preschool services implemented after baseline. The 35 target children who reported preschool enrollment in the control group were distributed in 18 different communities.

Complementing this evidence, our qualitative survey to caregivers in treated communities reveals high levels of their children’s school attendance and satisfaction with the services provided. In fact, 91% of the caregivers interviewed reported that their children attended the preschool center throughout the school year, and 89% reported that their children attended the escolinha 5 days a week. In addition, 95% of the caregivers interviewed reported that their child was happy to attend the escolinha. This high satisfaction level have been a mechanism the of the on may potential driving impact program preschool enrollment.

# **6.2 Overall effects on primary-school success and skills**

The DICIPE construction had and on preschool program large positive impacts primary school outcomes, as shown in Table 2. Children in treated communities are six percentage points more likely to enroll in primary school (Column (2)) and three percentage points less likely to repeat a school grade (Column (3)) than children in the control group. Moreover, using the age of the children at the end of the study and the current grade reported at school, we constructed an indicator for attending the appropriate grade for the age. Column (4) reports that treated children are 5.7 percentage points more likely than the control group to have the appropriate grade for age. All our estimates are statistically significant, with p-values adjusted for multiple hypotheses testing (henceforth MHT) ranging from 0 to 0.055.

In column (5) of Table 2, we present ITT impacts on a primary school enrollment index that was constructed over current primary school enrollment, grade repetition, and gradefor-age appropriateness. We find that overall, treated children have a primary school enrollment index that is 0.16 SD higher than control children—estimate which is signifthe treatment group.

20
Bongini, Claessens and Ferri (1999) investigate the Asian crises by focusing mostly on individual institution data. Specifically, they analyse how CAMEL variables, bank size, and corporate connections, as well as country dummies, explain bank failures. They find that CAMEL variables do reasonably well in predicting distress, that big financial institutions are more likely to become distressed but less likely to be closed, and that connected institutions are more likely to experience trouble. They conclude that while exogenous shocks played a role in causing the systemic crisis in Asia, there were also significant prior weaknesses at the individual bank level that contributed to distress.

# _Financial liberalisation and crises_

The view that financial liberalisation may lead to greater financial fragility has been often articulated (Caprio and Summers, 1993; Stiglitz, 1994; see also Allen, 2005, this volume). Financial liberalisation gives banks greater opportunities to take on risk. With limited liability and implicit and explicit guarantees, when bank capital and charter value erode, bankers do not bear much downside risk. Unless the country has well developed institutions and good prudential regulation and supervision to curb risk-taking, liberalisation may increase fragility beyond socially desirable limits.

Demirgüç-Kunt and Detragiache (1999) find that banking crises are indeed more likely to occur in countries that have liberalised their financial systems, even after controlling for other country characteristics. This effect, however, is mitigated by a strong institutional environment, especially respect for the rule of law, low corruption and good contract enforcement. These results are consistent with the view that if liberalisation is not accompanied by sufficient prudential regulation and supporting institutions to ensure effective supervision, it is likely to result in excessive risk-taking and a subsequent crisis. Later empirical studies by Mehrez and Kaufmann (1999), Glick and Hutchison (2001), Arteta 15
) / Food systems<br>dashboard|
|**NCD-Risk**score among adults ≥ 15|NCD-Risk scores are on a scale of 0 to 9; higher scores indicate greater risk for noncommunicable diseases.<br>Data are for individuals ≥15 years of age from the 2021 Gallup World Poll for 2022|World Gallup Poll<br>(GDQP) / Food systems<br>dashboard|
|Prevalence of**overweight & obesity**<br>among women (20 - 49 years) %|Percentage of women 20–49 years of age with a BMI greater than or equal to 25 kg/m2 in 2016|UNICEF Global Database<br>(based on NCD-RisC)|
|Prevalence of**underweight**among<br>women (20 - 49 years) %|Percentage of women 20–49 years of age with a BMI less than 18.5 kg/m2 in 2016|UNICEF Global Database<br>(based on NCD-RisC)|
|Adult**diabetes**prevalence|Proportion of adults aged 18+ years with diabetes. Diabetes is defined as having a fasting glucose of 7.0<br>mmol/L or higher, being on medication for raised blood glucose, or having a past diagnosis of diabetes. Data<br>for 2014, except 1 country 2003, 1 country 2005, 2 countries 2010, 1 country 2012.|NCD-RisC / Food systems<br>dashboard|
|Adult**raised blood pressure**<br>prevalence|Percent of adult population (18+ years) with systolic blood pressure ≥ 140 mmHg or diastolic blood pressure<br>≥ 90 mmHg in 2015|NCD-RisC / Food systems<br>dashboard|
|**Gini**coefficient|Index of inequality, 0=perfect equality, 100=perfect inequality, latest year available (ranges by country from<br>2010 to 2021)|World Bank|
|**Region**=
TFP. Given _Doing Business_ ’s tendency to capture a firm’s de jure access for a typical small and medium enterprise (SME), we would place a greater weight on the findings on the Enterprise Survey and Global Competitiveness indicators of access to finance.

_Barriers to Entry._ There is some evidence that entry barriers are associated with lower productivity. The Enterprise Survey’s Land Acquisition Obstacle indicator is negatively and significantly associated with productivity. Reducing it by one standard deviation is associated with a 3-percentage point increase in productivity. The Doing Business Construction Permit measure, however, is not significantly associated with productivity.

To summarize, the three groups of indicators offer independent sources of variations in explaining firm performance. The Enterprise Survey indicators seem to work best in explaining firm performance; they explain the largest share of variations in firm performance. Their qualitative results best confirm our priors. For instance, corruption is negatively associated with firm growth when using the Enterprise Survey indicator, but positively associated when using the Global Competitiveness indicator. Physical safety is positively associated with firm performance according to the Enterprise Survey indicator but is insignificant according to the Global Competitiveness indicator. Access to finance is positively associated with firm performance in the case of the Enterprise Survey indicator, but insignificant in the case of the Doing Business indicator. Physical safety and ease in land access have significant and positive effects only in the case of the Enterprise Survey indicators.

By comparing the three sets of estimates, we are able to modify our priors on the effects of the BE. In particular, we increase our confidence concerning four aspects of the BE (given that findings from at least two sets of data are robust and the third set does not contradict the other two): access to finance (DB and ES); electricity/basic infrastructure (ES and DB); internet/modern infrastructure (ES and GC); and human capital (DB or World Bank HLO, ES, and GC). We are less sure about the negative effect of corruption
_American Economic Review_ 119 (10): 3071-3099.

- Dixit, Avinash. 1997. “Power of Incentives in Private versus Public Organizations.” _American Economic Review_ 87 (2): 378-382.

- Donaldson, Dave and Adam Storeygard. 2016. “The View from Above: Applications of Satellite Data in Economics.” _Journal of Economic Perspectives_ 30 (4): 171–98.

- Elvidge, Christopher, Daniel Ziskin, Kimberly Baugh, Benjamin Tuttle, Tilottama Ghosh, Dee Pack, Edward Erwin, and Mikhail Zhizhin. 2009. “A Fifteen-Year Record of Global Natural Gas Flaring Derived from Satellite Data.” _Energies_ 2 (3): 595–622.

- Evans, Peter B., and James E. Rauch. 1999. “Bureaucracy and Growth: A Cross-National Analysis of the Effects of ‘Weberian’ State Structures on Economic Growth.” _American Sociological Review_ 64 (5): 748-765.

- ----. 2000. “Bureaucratic Structure and Bureaucratic Performance in Less Developed Countries.” _Journal of Public Economics_ 75 (1): 49-71.

- Fan, Gang, Xiaolu Wang, and Hengpeng Zhu. 2010. _Marketization Index for China’s Provinces (2009)_ . Economic Science Press.

- Fang, Hanming, Quanlin Gu, and Li-An Zhou. 2019. "The Gradients of Power: Evidence from the Chinese Housing Market." _Journal of Public Economics_ 176: 32-52.

- Finan, Frederico, Benjamin A. Olken, and Rohini Pande. 2017. “The Personnel Economics of the Developing State.” In _Handbook of Economic Field Experiment,_ ed. By Aghijit V. Banerjee and Esther Duflo, 2: 467-514.

- Fisman, Raymond, Jing Shi, Yongxiang Wang, and Weixing Wu. 2020. “Social Ties and the Selection of China’s Political Elite.” _American Economic Review_ 110 (6): 1752–1781.

- Fukuyama, Francis. 2014. _Political Order and Political Decay: From the Industrial Revolution to the