rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 7831

# **Abstract**

This analysis examines the relationship between nonrenewable resource dependence, economic growth and income inequality. It uses a two-equation system in which the Gini index and GDP per capita are the dependent variables and the stock of nonrenewable resources as a share of national wealth—i.e. resource dependence—is the independent variable. Using a dataset that includes information on 43 countries from 1980 to 2012, this paper estimates several model specifications in order to check the robustness of the results under different assumptions and to account for income-group-related heterogeneity among countries. The baseline model provides strong evidence that natural resource dependence is negatively correlated with both per capita GDP and the Gini index; in other words, resource dependence is associated with lower income levels, but also with a more equal distribution of income. Interestingly, however, after controlling for country income group, the sign and magnitude of these relationships appear to become dependent on national-level structural characteristics. Among higher-income countries, greater nonrenewable natural resource dependence is associated with lower income inequality, while there is no statistically significant correlation with GDP per capita. Among the lower-income group, greater dependence on nonrenewable natural resources is associated with both higher levels of income inequality and lower per capita GDP. Further analysis focusing on a subsample of nonrenewable resource rich countries confirms these findings.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at gmele@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. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Schady (2005) analyze the effects of the crisis of the late 1980s in Peru on infant mortality using DHS data. They show that the crisis coincided with a large spike in infant mortality—from 50 to 75 per 1,000 children born. Using these estimates they calculate that the crisis resulted in approximately 18,000 “excess” deaths—deaths that would not have taken place without the economic crisis.<sup>22</sup> Paxson and Schady (2005) make two other points. First, they show that the increase in mortality was not a result of possible changes in the composition of women giving birth.<sup>23</sup> Second, they show that the crisis came hand in hand with a dramatic collapse in public expenditures on health, which fell from approximately 80 Peruvian soles per capita in 1988 to 30 soles in 1990. Using the DHS data, Paxson and Schady also show declines in health service utilization by households during the crisis years, including a higher fraction of home births and fewer antenatal check-ups. They conclude that the health sector collapse may be part of the explanation for the spike in infant mortality. We return to this point below.

Cutler et al. (2002) use vital registration data to analyze the effects of economic contractions on mortality in Mexico between 1980 and 1998.<sup>24</sup> They focus on three crisis periods, corresponding to 1982-84, 1985-89, and 1994-96. To quantify the impact of these crises on the mortality of vulnerable groups, in particular children (age 0-4) and the elderly (age 60+), Cutler et al. assume that the mortality of prime-age males, age 30-44, was unaffected by the crises. They then compare changes in mortality among children and the elderly before and during the crisis with the corresponding changes in mortality among prime-age males. Based on this difference-in-differences estimation strategy, Cutler et al. conclude that the three crisis periods resulted in increases in child mortality of 9.2 percent (1982-84 crisis), 10.3 percent (1985-89)

> 22 In the Working Paper version of their paper, Paxson and Schady (2004) also provide evidence of a deterioration in height-for-age for young children during the crisis.

> 23 Paxson and Schady (2005) use Oaxaca-style decompositions to show that changes in the composition of women giving birth along various observable characteristics, such as education and place of residence, are small, and that holding constant these characteristics has little effect on their estimates of infant mortality during the crisis. 24 The use of vital registration data is a source of concern. In most developing countries, including Mexico, underreporting of deaths is common. Moreover, if, as seems plausible, the coverage of the vital registration system itself changes with aggregate economic conditions, the estimates may be systematically biased. For example, if fewer deaths are reported or accurately recorded during bad economic times, the associations reported by Cutler et al. (2002) would be downward biased.

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standardized geo-coordinates, which were collected in the LSMS-ISA survey data.<sup>8</sup> Farm location was used as a centroid to construct several variables covering the area within 20km. The average percent tree cover within a 20km radius of each household was derived using tree cover from MODIS Vegetation Continuous Fields (MOD44B) data (DiMiceli et al., 2011). An indicator of population density based on the number of people per km<sup>2</sup> within 20km of each household was created using data from the Global Rural-Urban Mapping Population Project (Balk et al., 2006; CIESIN et al., 2011). To construct a variable on the average percentage of fertile soil within 20 km of each household, we combined information from the FAO/UNESCO Digital Soil Map of the World (FAO/IIASA/ISRIC/ISSCAS/JRC, 2012) and Nunn and Puga (2010) estimates of percentage of land surface area with fertile soil.<sup>9</sup> Finally, we used two control variables— household specific measures of annual mean temperature (C) and average annual precipitation— that were created using a standard methodology by the World Bank LSMS-ISA team and made available for all LSMS-ISA surveys (see World Bank, 2015 for details).

Finally, all models included a set of country fixed effects to account for country-level unobservables (∑���� ������. They were also rerun with district fixed effects, purging the estimated coefficients from potential unobserved district level variables (such as relative price differences across crops or market access). Table 1 summarizes the main descriptive statistics for all covariates. 3 Prevalence, Economic Contribution, and Determinants of Trees on Farms

- 3.1 One‐Third of Smallholder Farmers Cultivated Trees

With on average 33% of all rural households reporting having at least one tree on their land, trees are clearly not marginal on the smallholder farms across the African countries studied (Table 2). Prevalence was highest in Tanzania (54%) and Ethiopia (38%) and lowest in Nigeria (16%). Further disaggregation by type of tree shows that Tanzanian farmers emphasize fruit trees (with 45% growing fruit trees,

> 8 LSMS-ISA surveys provide a modified coordinate to protect household confidentiality, by introducing a random distortion of 0-5km from the original location of the rural household. For more details on this type of method and its implications for statistical inference see Perez-Heydrich et al. (2013).

> 9 Defined as soil that is not subject to severe constraints for growing rain-fed crops in terms of soil fertility, depth, chemical and drainage properties, or moisture storage capacity.

9
Tree line shift is a potential indicator of climate-driven ecosystem changes in mountain regions (Guisan et al, 1995). Tree line position is strongly correlated to various climatic parameters and is highly sensitive to climate change, making it a useful indicator of climate change (Grace et al., 2002). Panigrahy et al. (2010) reports a shift of around 300 m in tree line elevation since 1960 using satellite data of the Nanda Devi Biosphere Reserve in Uttrakhand, although the data quality has been questioned (Bharati et al., 2011). Another study in the western Indian Himalayas records an upward shift of tree line species at 19 and 14 m/decade on south and north slopes, respectively (Dubey et al. 2003). INCCA (2010) discusses an uncited systematic study of climate change impact on 11 multipurpose tree species in Himachal (western Indian Himalayas) that notes substantial shifts in critical phenophases (e.g. leaf emergence, flower initiation and growth period etc.) over an eight-year period. In a related study using repeat photography of Baima Snow Mountain in China’s Yunnan Province, the tree line moved up 67 m in elevation and 270 m upslope from its 1923 location (Baker and Moseley, 2007). These local observations in the Himalayas generally agree well with observations in other mountain regions, such as the Swiss Alps (Gehrig-Fasel et al., 2007).

In the Bhutanese Himalaya, high frequency of pine die-back (five counts from 19922008) is associated with periods of higher temperature and lower rainfall (NBC, 2011; Wangda et al., 2009). Similarly, although most forest fires in the region are human-caused (GFMC, 2011), increasing fire frequency and related losses are likely caused by higher fire vulnerability due to prolonged dry winter conditions (BAP, 2009). Other observations in Bhutan also point to increasing climate influence on forest fires, including a large forest fire in a region with no known prior fire experience in 1998-1999 (BAP, 2009), and a temporal correlation between higher incidences of forest fire and low rainfall (e.g. 2004-2005). Although no detailed information is available in the Nepalese or Indian Himalayas, the trend is expected to be similar to that observed in Bhutan.

Climate change can impact at species level by altering reproduction, migration pattern, and frequency and severity of pest, invasive species, and disease outbreaks (Campbell et al., 2009). Similarly, climate change can create favorable environments for colonization by invasive species in terrestrial and fresh water ecosystems (Campbell et al, 2009). Apparently, there are no published systematic studies or data on species-level impacts of climate change in the Himalayas.

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# **1. Introduction**

Digital technology has become common in the workplace. Nearly two-thirds of workers in the European Union reported using a computer or smartphone at work in 2015 (OECD 2019). Computer use at work is less widespread but still prevalent in low- and middle-income countries: 30 percent of respondents in 10 middle-income countries reported using computers at work in 2012-13, ranging from 34 percent in urban Vietnam to 55 percent in Yunnan, China (Saltiel 2020). Ride-hailing and digital payment apps have made smartphones an important tool in even the most elementary occupations, such as motorbike taxis and small shopkeepers, in developing countries around the world.

The COVID-19 pandemic accelerated the adoption of digital technologies in workplaces. In response to the pandemic, firms in low- and middle-income countries, including in Southeast Asia, increased their use of and investments in digital platforms and solutions, such as e-commerce technology and online payment systems (Apedo-Amah et al. 2021; DeStefano and Timmis _forthcoming,_ discussed in World Bank 2021). Firms looked for ways to facilitate remote work for workers who could do their jobs from home.

Despite these trends, there is limited evidence on the skills needed to accompany the increasing digitalization at work, especially in low- and middle-income countries. While Southeast Asian countries are anticipating an Industrial Revolution 4.0 driven by the rise of digital technologies, there is little information about how these technologies will change the task content and skills requirements of jobs. Demand for digital skills is almost certain to rise. However, the types of digital skills required, and the extent to which occupations outside of the information and communications technology (ICT) sector will require digital skills, remains unclear. Furthermore, there is limited information on the extent to which other crucial skills, specifically cognitive skills (general knowledge and mental abilities) and socioemotional skills (behaviors and attitudes to manage emotions, relationships and personal goals), are needed to learn and manage digital technologies required in the workplace.

Previous work using data from high-income countries provides evidence of the complementarity among digital, cognitive, and socioemotional skills. Research using the PIAAC surveys of adults in 30 countries (mostly high-income) shows that workers using more digital skills at work also have higher basic cognitive skills (e.g. numeracy) and perform more tasks that require cognitive and socioemotional skills, such as management and communication, accountancy and sales, and negotiation (OECD 2016, 2019). ICT occupations, such as software and application developers and database professionals, require more advanced digital skills (e.g. programming). However, the PIAAC data also show that workers in ICT occupations perform many tasks that require cognitive and 2
Figure 6: Trends in Students’ average Academic Performance by Cohort: 2010–2014 <!-- Start of picture text --> 61<br>60<br>59<br>58<br>16−18 years old cohort<br>19−23 years old cohort<br>57<br>2010 2011 2012 2013 2014<br>Academic Year<br>Student Overall Weighted Mean (OWM)<br><!-- End of picture text --> _Source_ : Own calculations using the NUL students records data for the academic years 2010, 2011, 2012, 2013, and 2014. _Notes_ : OWM is the overall weighted mean.

between the FPE-treated and FPE-untreated groups (Abadie, 2005). In such a case, therefore, the FPE effect identified by the standard DID would be biased. It is, therefore, important to check whether the pre-PFE characteristics are balanced between the control and treatment groups. Table 2 presents the pre-FPE compositional differences between the control and treatment groups. Specifically, the table shows pre-FPE mean differences, by treatment group, in the main outcome variable (OWM) and the individual characteristics (APS, age, gender, and location). Column (3) shows the _p−_ value of the _t−_ test for differences in mean characteristics of the control and treatment groups pre-FPE. Looking at these _p−_ values, we can see that there are statistically significant differences between the control and treatment group with respect to their age, pre-university (i.e. COSC) academic performance, gender and location (e.g. Berea and BothaBothe district).<sup>12</sup> Undoubtedly, these characteristics are associated with the changes in student’s academic performance over time. For example, because early and current investments in human

> 12Column (5) of the table presents the normalized difference in characteristics between groups. The normalized difference between means is a scale-free measure ̄ of the difference in the distribution of characteristics by treatment status, and is given as Norm-diff _≡△_ **X** = _~~√~~_ _<u>XS</u>_ <u>10</u> _−_<sup>2+</sup> _<u>X</u>_<sup> ̄</sup><sup>_S_</sup> <u>01</u><sup>2,where</sup><sup>_S_</sup> _G_<sup>2isthesamplevarianceof</sup><sup>_Xi_insub-samplewith</sup> treatment _Gi_ = _{_ 0 _,_ 1 _}_ . If the normalized difference is less than one quarter in absolute value, the unconfoundedness assumption is likely satisfied and the model is less sensitive to functional form (Imbens and Wooldridge, 2009). Using this indicator, we can see that pre-FPE performance at the COSC exams (pre-university entry exams) is different between groups.

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18 Table 1: Deviation of national NRA around its trend value,<sup>a</sup> 12 key covered farm products,<sup>b</sup> developing and high-income countries, 1965-72, 1972-76, 1976-84, 1984-88 and 1988-2004 (NRA percentage points)

|||Develo<br>|ping countr<br>|ies<br>|||High-in<br>|come countr<br>|ies<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||1965-<br>1972|**_1972-_**<br>**_1976_**|1976-<br>1984|**_1984-_**<br>**_1988_**|1988-<br>2004|1965-<br>1972|**_1972-_**<br>**_1976_**|1976-<br>1984|**_1984-_**<br>**_1988_**|1988-<br>2004|
|**Grains, oils, sugar**|||||||||||
|Rice (28%)|24|**_32_**|36|**_62_**|64|54|**_48_**|89|**_222_**|227|
|Wheat (18%)|30|**_29_**|37|**_45_**|46|44|**_18_**|67|**_119_**|84|
|Maize (7%)|38|**_34_**|37|**_53_**|28|42|**_28_**|43|**_76_**|53|
|Soybean (3%)|36|**_30_**|56|**_75_**|124|10|**_11_**|111|**_142_**|41|
|Sugar (42%)|70|**_38_**|46|**_95_**|57|216|**_45_**|133|**_230_**|160|
|**Tropical cash crops**|||||||||||
|Cotton (-5%)|30|**_39_**|42|**_38_**|32|63|**_42_**|11|**_21_**|29|
|Coconut (-21%)|23|**_17_**|22|**_15_**|22|na|**_na_**|na|**_na_**|na|
|Coffee (-12%)|31|**_37_**|50|**_44_**|25|na|**_na_**|na|**_na_**|na|
|**Livestock products**|||||||||||
|Milk (88%)|68|**_61_**|87|**_101_**|61|187|**_188_**|281|**_327_**|156|
|Beef (43%)|32|**_38_**|59|**_63_**|51|54|**_57_**|103|**_167_**|117|
|Pigmeat (3%)|96|**_84_**|73|**_49_**|61|75|**_61_**|75|**_98_**|74|
|Poultry (21%)|111|**_90_**|112|**_63_**|76|44|**_46_**|135|**_221_**|192|

a Deviation is computed as the absolute value of (residual – trend NRA) where trend NRA in each of the two sub-periods is obtained by regressing NRA on time. The global NRA average for each product for 1985-2004 is shown after the product name in column 1.

b Unweighted average of national deviations.

Source: Anderson et al. (2010).
# 5.1 Malaria Policies Toward Travelers

The NGO PATH has specically focused on reducing imported cases in the district Richard Toll. They have used volunteers in the community to alert health workers to the arrival of new travelers. Health workers track down these travelers and ask to test them using an RDT, and treat those that test positive. Based on 2015 data provided by the Richard Toll Health District Director, 3,609 people were identied as travelers, of these 3,386 were tested and 10 tested positive for malaria. In 2015, there were a total of 186 imported cases; therefore, this strategy was only able to detect 5% of imported cases.<sup>25</sup> A more systematic policy to target travelers was implemented in one health post in Richard Toll, which is privately run by the Senegalese Sugar Company (CSS) for its workers and their families. CSS hires over 3000 migrant workers every year to help with the sugar harvest. Malaria was a large burden for the company, causing lower productivity, high absenteeism, and high spending on pharmaceuticals to treat it (Djibo and Ndiaye 2013). In late 2011, the CSS implemented a new mandatory policy for all seasonal workers: testing every worker at the beginning of the season using an RDT, treating anyone testing positive, and providing workers and their families with bednets and information. There was a drastic decrease in cases after the implementation of this policy, with case numbers at zero or close to zero after the policy (Figure 5). Data on two types of schistosomiasis among workers at the CSS show no drop in those diseases after late 2011, demonstrating that the drop in malaria cannot be attributed to an overall improvement in the health care facility. This closely mirrors the outcomes measured by Dillon, Friedman, and Serneels (2014), who nd that a policy oering testing and malaria treatment for workers at a sugarcane plantation in Nigeria leads to a 10% increase in earnings due to increased labor supply and productivity.

A strategy to decrease malaria cases that could be harnessed for targeting travelers is proactive community treatment (ProACT) implemented by trained home care providers (HCPs). The pilot of this intervention consisted of HCPs going door-to-door weekly to

> 25Data on imported cases is based on surveys conducted in Richard Toll.

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Hence, understanding the duration of a credit boom is of crucial importance given its consequences on the financial sector and economic activity. For instance, Barajas, Dell’Ariccia and Levchenko (2009) and Dell’Ariccia et al. (2012) examine whether the duration of credit booms is important in predicting future financial crisis or not. In fact, the literature finds that the duration (as well as the amplitude) of the credit booms is a robust predictor of those booms that end up in a systemic banking crisis.

The main goals of this paper are: first, to investigate whether the likelihood of credit boom episodes ending depends on its own length of time, i.e. whether they are duration dependent or not; and, second, to analyze whether there are breaks in its behavior employing a Weibull duration model with change-points. Castro (2012) adapts the Weibull model with change-points proposed by Lara-Porras et al. (2005) to examine the duration of the business cycle phases. Therefore, we follow his strategy in employing a continuous-time Weibull model with change-points to analyze the duration of credit booms.

Our paper uses the quarterly gross capital flows data for 71 countries from 1975q1 to 2010q4. Following Rothenberg and Warnock (2011), Forbes and Warnock (2011), and Calderón and Kubota (2012a, 2012b), we argue that the dynamics of capital flows and credit markets along the business cycles are better captured using quarterly data.

The identification of credit boom episodes follows the work undertaken by Calderon and Kubota (2012b). Given that there is no single criterion to identify credit booms, they use: (i) the Mendoza and Terrones (2008) criteria (MT-criteria) and (ii) and the Gourinchas, Valdes and Landarretche (2001) criteria (GVL-criteria). The first criterion identifies a credit boom when the deviation of the real credit per capita from its trend exceeds 1.75 times its standard deviation, whilst the second one considers that a credit boom takes place if the deviation of the ratio of credit to GDP from its trend exceeds 1.5 times its standard deviation or the (year-on-year) growth in the credit-GDP ratio exceeds 20 percent. The paper also differentiates regular booms vis-a-vis those that end up in a systemic bank crisis or _bad_ credit booms. Empirical evidence shows that not all credit booms end in a full-blown banking crisis and that a large share of them are followed by a soft landing (Tornell and Westermann, 2002; Barajas et al. 2009; Calderón and Servén, 2011). Consequently, we define bad credit booms 3