rafmacalaba/data-use-mentions-extended huggingface dataset:
interactions with border ruggedness to see if they affected famines either independently or through border ruggedness. Because our key prefecture-level indicators are not time-varying, we cannot include in these regressions the prefecture fixed effects. We thus include year and warlord fixed effects to control for the time trend and warlord-specific factors.<sup>13</sup> The warlordfixed effect reflects a fixed region with a unique warlord-ruling history: In the case of rule by one warlord, it captures his ability and policy benevolence; in the case of rule by multiple warlords, their ability and policy benevolence.

We cluster standard errors by warlord to allow for correlations among the error terms from prefectures controlled by the same warlord. In columns 1 and 2 of Table 7, both variables and their interactions with border ruggedness are insignificant, indicating that differential population composition is unlikely to be a channel for the link between famine and border ruggedness.

# **5.3.  Fiscal Capacity Is Not a Mechanism**

Apart from affecting the ruler’s incentives, border ruggedness could increase the warlord’s resources and thus the incidence of famines. For instance, a warlord surrounded by rugged terrain might need to spend less resources in military affairs, and would therefore have more resources at his disposal, and he would be less constrained in his ability to provide for his people. We should point out that, in the long run, differences in fiscal resources could be part of the stationary bandit effects, so our concern here is that the difference in fiscal resources merely reflects pre-existing endowment before the warlord fragmentation period. If so, our previous findings on the negative relation between border ruggedness and famine incidence may reflect not his calculation on his ruling horizon but his endowment.

To address this possibility, we consider two proxies for local resource constraints. The first is the amount of local taxes. We have data on prefectural business taxes _(Likin_ ) and land taxes, the two major sources of local fiscal income, in the year 1925. The second proxy for
# **3. Empirical Strategy**

# **3.1. (Individual-Level) Ownership of and Rights to Agricultural Land**

This section describes the empirical framework for estimating relative survey treatment effects that the concurrent implementation of IHS4 and IHPS can isolate. The core specification is estimated for the total sample, and separately, for the sub-populations of men and women as: 1τ + γ (1) iih 1iih iih yy = ∝ + ββ + εε where _i_ and _h_ represent individual and household, respectively; _y_ is the binary dependent variable on whether the individual has ownership/rights (detailed in Table 2) over any agricultural parcel in the household; _α_ and _ɛ_ represent constant and error terms, respectively. 1 is a binary variable identifying the adults in the IHS4 sample, with the individuals in the T5/IHPS constituting the comparison category. _C_ is a vector of individual and household attributes presented in Appendix ττ Table A2 to capture any remaining unobserved heterogeneity that may also jointly determine both the dependent variable and household assignment to the IHPS versus the IHS4.

Given the dichotomous nature of the dependent variables, equation (1) is estimated as a linear probability model with weights adjusting for non-response.<sup>23</sup> The T5/IHPS sample is used as the comparison category in equation (1) as it represents the gold-standard in the approach to data collection on asset ownership and rights. Standard errors are clustered at the EA-level, and the regressions are weighted using the response weight variable, as described in Section 2.2.

Since the focus of the analysis is on ownership/rights over agricultural land and in part on the SDG indicator 5.a.1, the reference population is adult individuals living in agricultural households, who have operated land for agricultural purposes and/or raised/tended livestock in the past 12 months, regardless of the final destination of the production. As noted above, the IHS4 sample includes all the adult household members, who are tagged as
access to childcare spend just as much time on unpaid work as those who do not have access to childcare, suggesting a significant double burden on this group. Having access to childcare is positively associated with women’s labor market outcomes and time spent on paid market work, suggesting that making childcare services more widely available could partly offset the adverse labor market impacts of motherhood, with potential implications for their social and economic well-being and empowerment.

The paper builds on the empirical literature on the relationship between childcare and women’s economic outcomes. Previous literature has focused predominantly on high- and middle-income countries and wage labor settings (Bick 2016, Givord and Marbot 2015, McCoy et al. 2017). Few studies consider low-income countries, vulnerable population groups, and other types of market work and unpaid work. Similarly, even though the link between women’s unpaid work and market work has been documented in some contexts (App and Rees 2005, Antonopoulos 2009, Charmes 2015, Samuels et al. 2018), the relative scarcity of time-use surveys in developing countries constrains the growth of this strand of literature. In South Asian countries, specifically, the literature has focused on identifying the determinants of low and declining FLFP in Bangladesh, India, and Sri Lanka, considering marriage, childbirth, and access to childcare among the possible drivers of these trends (Das 2006, Rani and Unni 2009, Bhalla and Kaur 2011, Gunatilaka 2013, Kapsos et al. 2014, Sorsa et al. 2015, Chatterjee et al. 2015, Solotaroff et al. 2019). The few studies that examine women’s labor market disadvantages in relation to childcare support, however, measure access to childcare indirectly, using household composition and other proxy variables (Madurawala 2009, Das and Žumbytė 2017, Solotaroff et al. 2020). Finally, studies that causally attribute the effect of childcare on women’s employment outcomes remain scarce, with a notable exception of an impact evaluation of a public child support program in India (Jain 2016
age population in better employment: (3) Better employment rate (BER) = (Non-agricultural wage employees+ employers)/Population (working age) The full UHCI is a weighted average of the HCI score for those in the better employment (who are as productive as their human capital allows), and the theoretical minimum HCI for the rest of the working age population. The theoretical minimum HCI ≈0.2 represents the relative productivity of “raw labor”, before it is boosted by human capital (see Box 1): (4) UHCI (full measure) = BER × H +(1-BER) × (minimum HCI) HH The full utilization rate is given by Equation 5 and is the full UHCI divided by the HCI. This means that the utilization rate is a weighted average of 1 (complete utilization for those in better employment), and reduced utilization rates for the rest of the working age population. The utilization rate for the rest of the working age population (1-BER) is the productivity of raw labor (HCI min) relative to potential productivity of those workers (HCI). While the full UHCI is my preferred measure because it is more comprehensive and realistic, it has the disadvantages of greater complexity, slightly reduced country coverage and taking a stand on the definition of better employment.

# (5) Utilization (full measure) = BER × 1 +(1-BER) × (minimum HCI)/HCI

Despite different methodologies, the basic and full measures produce broadly similar utilization rates and UHCI scores. They are both available for around 165 countries, and the average UHCI score is about 2/3 the size of the HCI (mechanically it will be lower). Utilization rates are roughly U-shaped in per capita income across countries, slightly declining with per capita income across low and middle income levels and the rising rapidly from middle to higher income levels. This feature of utilization rates implies that UHCIs are low in the poorest countries, where

> 3 This definition of “
32 economies, then this may lead estimates of the gap to be biased upwards. However, Web Appx. Fig. 7(b) shows that the number of hours worked does not significantly increases with experience, whether in developed or developing countries. Thus, any training that could differentially affect the returns in developed vs. developing countries would be at work.

To see whether the differences in the experience premium might be accounted for by differences in hours spent training, we draw on recent data from _OECD.Stat_ (OECD, 2021). The data contain information on 33 developed economies and 4 developing economies.<sup>51</sup> Let us assume that all this time was compensated, and that none of it involved training that was performed simultaneously with production. Assuming the data are reasonably representative of developed and developing economies, we can use them to explore the impact of any adjustment of hours to account for time spent in formal or informal training on our measured gaps in the returns. To do this, first we obtain an estimate of the share of time that workers spend in training. Then, given that the Ben-Porath framework has human capital accumulation concentrated in the early years, we explore different scenarios for the years in which this training is concentrated.

First, in developed economies 59% of workers report spending time formally and/or informally training, compared to 31% in developing economies. On the other hand, when they do train, workers train slightly more in developing economies than in developed economies, 154 hours compared to 120 hours. This implies that a typical worker obtains 71 hours of training per year in developed economies, compared to 48 in developing economies. According to I2D2 data, workers in developed economies work 43 hours per week on average compared to 50 in developing economies. Assuming three weeks of time off, we get that workers in developed economies on average train for 3 _._ 4% of their working time, compared to 2 _._ 0% in
big cities and smaller towns changes satelliteyear by satellite-year.

# **IV. The Econometric Model**

Spatial autocorrelation in levels of, and changes in, poverty was noted in Section 3.1, and our econometric modeling recognizes this by using the spatial panel estimator of Belotti et al (2014). The regional _N_ -vector of poverty measures for date _t_ (=1,..., _T_ ) is denoted<sup>_P_</sup> _t_<sup>and</sup> the matrix of explanatory variables is _X t_ which includes our measures of urban economic activity as indicated by the night lights data. Our spatial Durbin model (SDM) can be written as:<sup>20</sup>

> 20 For an excellent overview of the spatial Durbin model see Elhorst (2012). Note that the model is static. A temporal lag of _Pt_ can also be included. We found that such a temporal lag of the NSS region poverty rate was statistically insignificant so we dropped it from the model. This allows us to keep the first year of our data.

13
|
|||(3.301)|(2.956)||(3.713)|(3.121)||(3.735)|(3.327)|
|||[0.000]|[0.000]||[0.000]|[0.000]||[8.161]|[2.736]|
|_R-squared_|0.032|0.173|0.342|0.041|0.185|0.350|0.040|0.172|0.312|
|# Observations|78144|78144|78144|78144|78144|78144|78144|78144|78144|
|# Schools|3075|3075|3075|3075|3075|3075|3075|3075|3075|
|# Countries|8|8|8|8|8|8|8|8|8|
|Country FE|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|School controls||Y|Y||Y|Y||Y|Y|
|Student controls|||Y|||Y|||Y|

Notes: Standard errors in parentheses, p-values in square brackets. OLS regressions for PISA were run with the student-level PISA dataset for 8 Latin American countries using the OECD’s repest Stata command. Standard errors clustered at the school level and use all 5 plausible values for each subject and _student final weights_ . All specifications include country fixed effects. School controls include school location, student-teacher ratio, log of the number of students, share of government funding relative to total school funding, and ratio of computers connected to the web as a proxy for school resources. Student controls include gender, grade, socio-economic status and immigration status. For control variables, missing variables are replaced with a value of -99 and we include an indicator variable
29 Figure 4. Correlations among Financial System Characteristics (cont’d) <!-- Start of picture text --> Financial Sector Depth: Institutions vs. Markets<br>0 100 200 300<br>(Stock Market Capitalization + Outstanding Domestic Private Debt Securities)/GDP<br>300<br>200<br>100<br>0<br><!-- End of picture text --> Source: Authors, based on the Global Financial Development Database. Notes: see Table 1.
relative to the self-employed and informal sector workers.

# **B. Empirical**

This paper contributes to the literature on the estimation of the magnitudes of earnings differentials between self-employed and informal sector employees relative to formal sector and salary employees in developing economies. Many of these studies are based on data from Latin America and the Caribbean and most focus on middle income countries. Almost universally, these studies find that workers in the informal sector earn less than equally qualified employees in the formal sector (i.e. Heckman and Hotz 1986; Gindling, 1991; Basch and Paredes-Molina, 1996; Launov, 2006; and Günther & Launov, 2012). However, not all informal sector workers are self-employed, and the self-employed may be very different from informal sector employees. In a review of the evidence from Latin America, Perry et al. (2007, p.6) conclude that the self-employed voluntarily opt out of the formal sector, while informal salaried workers are queuing for more desirable jobs in either the formal salaried sector or as self-employed workers.

When researchers estimate formal-informal wage differentials separately for informal sector employees and self-employed workers, they typically find different results for the two groups. Compared to formal sector wage and salary employees, Arias and Khamis (2009) find an earnings penalty for informal wage and salary employees but an earnings premium for self-employed workers in Argentina. Nguyen et al. (2013) find the same result in Vietnam. Using quantile regressions, Nguyen et al. (2013) further find that both informal 7
Policy Research Working Paper 9969

# **Abstract**

This paper examines the performance of infrastructure companies owned by the state, using the newly created World Bank Database of Infrastructure State-Owned Enterprises (SOEs). The data cover 19 countries and 135 SOEs between 2000 and 2018. The analysis reveals that infrastructure SOEs are large and have weak financial performance that generates significant fiscal risk. The paper introduces new measures of financial performance net of fiscal transfers and examines previously uncovered patterns of subsidies by sector. It examines the effect of state ownership by comparing the firms in the database with hundreds of comparable private firms, using coarsened exact matching. The findings show that relative to comparable private firms, infrastructure SOEs are less efficient, represent a larger share of gross domestic product, have larger liabilities as a share of gross domestic product and larger employment costs as a share of revenues, and yield lower returns on assets.

This paper is a product of the Infrastructure Chief Economist Office. 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://www.worldbank.org/prwp. The author may be contacted at mdappe@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
new subway lines, and improved peripheral roads to divert traffic from congested areas. Extensive research has assessed the impact of mass transit measures on traffic and pollution (Adler et al. 2016; Anderson 2014; Basagaña et al. 2018; Gendron-Carrier et al. 2018; Goel and Gupta 2015; Tabatabaiee and Rahman 2011; Yang et al. 2017). Although magnitudes vary considerably, these studies all find significant traffic and/or pollution impacts.

# **2.4 Traffic Congestion and Air Pollution in Dar es Salaam**

Limited research has documented the rapid growth of motor vehicle traffic in Dar es Salaam, and its consequences for traffic congestion. Kiunsi (2013) draws on Tanzania Revenue Authority records to report that a total of 1,010,732 cars registered between 2003 and 2011, with Dar es Salaam accounting for 60% to 70% of the total. Similarly, Elinaza (2012) reports that Dar es Salaam absorbed most of the 245,180 motorcycles and 7,408 motorized tricycles registered in 2010 and 2011.

One major consequence of rapid growth in motor vehicle use has been severe traffic congestion. A 2007 JAICA study finds major slowdowns on primary traffic arteries under peak morning and evening conditions (Kiunsi, 2013). Congestion has in turn generated significant economic losses. Katala (2019) estimates a total loss of T Sh 655 billion per year from congestion-related delays and extra fuel use. Elinaza (2010) cites a Confederation of Tanzania Industries study which finds that traffic congestion reduces business profits by about 20 percent. Othman (2010) and Lupala (2010) identify congestion as a major source of air pollution in the city. Recent years have witnessed major investments to address these problems in Dar es Salaam, particularly an extensive bus rapid transit (BRT) system. Chengula and Kombe (2017) report that the BRT has provided significant benefits in time-saving, convenience and comfort. Nevertheless, serious congestion has persisted in Dar es Salaam as the number of motor vehicles has continued to grow.
Policy Research Working Paper 10715

# **Abstract**

Environmental degradation is the largest public health challenge of the century and is likely to be exacerbated by climate change. This study undertakes a comprehensive examination of the health implications of environmental hazards in Cambodia, simultaneously addressing extreme temperatures, precipitation patterns, and air pollution. It leverages data from the Demographic and Health Surveys and satellite-derived metrics on temperature, precipitation, and fine particulate matter. The analysis identifies a positive association between temperature and the occurrence of diarrhea and cough among children and a nonlinear relationship between precipitation and these health outcomes. Furthermore, the study demonstrates that pollution significantly impacts cough incidence. To anticipate future trends, climate simulations are employed to forecast the incidence of child diarrhea in Cambodia under different climate and development scenarios. The projections indicate that diarrhea incidence could increase to 19 percent by 2040 without significant adaptation measures that would lessen the adverse impact of weather. For instance, the acceleration in toilet ownership over the last decade reduced diarrhea incidence by at least 1.2 to 1.4 percentage points. Nevertheless, the path ahead requires proactive efforts to improve sanitation and hygiene. The forecasts suggest that, without additional strategies to counter climate change’s adverse effects, only universal toilet ownership would contain the climate-driven increase in diarrhea incidence expected by 2040.

This paper is a product of the Health, Nutrition and Population Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at sbaquie@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
comes from employing forced migrants in the lower ranks of these organizations or from charging them extortion fees en route (Insight Crime, 2023a,b, 2019). For example, one report states that “(...) Venezuelans have to choose between hunger or joining the ranks of organized crime groups, which is helping them strengthen and reorganize their criminal structures while also facilitating the spread of their illegal activities into Venezuela.” (Insight Crime, 2018).

We examine these hypotheses using longitudinal municipal-level data on violent and criminal events by organized crime groups and non-state armed actors. Table 3 presents the results. Our findings largely support these hypotheses. They imply that forced migration is associated with an increased number of such events by the largest non-state armed actors from Colombia, including the Revolutionary Armed Forces of Colombia (FARC) and the National Liberation Army (ELN), both of which are heavily involved in drug trafficking. The findings also indicate an increase in violent events linked to Venezuelan organized crime groups, including gangs such as _El Tren de Aragua_ , which is known for its involvement in human trafficking.<sup>24</sup>

> 24We also examine the effects on irregular armed groups, such as small street gangs and criminal organizations not related to drugs, human smuggling, or the regime, and find no effects, demonstrating that the results are not due to an increase in overall crime.

38