rafmacalaba/data-use-mentions-extended huggingface dataset:
differences with respect to long-term averages for a particular month.

Closely related are prices of staple food items, whose composition may differ between countries. We constructed a staple food price index to capture the market conditions for food commodities. Volatility in food prices are seen as major drivers for food insecurity (IPC Global Partners, 2019). The treatment of the raw data is discussed by (Andrée et al., 2020). We obtain food price data from the FAO and the WFP. We additionally include price data that are seasonally adjusted.

Political instability and conflicts have been other key drivers of famines in recent history, even with ample food supply. For instance, Devereux (2000) argues that famines in Sub-Saharan Africa were the result of the combination of natural disasters, such as droughts, and political triggers, such as civil wars. We gather data on conflict events and the number of associated fatalities from Armed Conflict Location & Event Data Project (ACLED). Due to the sparsity of conflict events, and their possible wider regional impact, we use inverse-distance weighting to proxy the effect of violent outbreaks close to a district of interest.

2To illustrate, the formula below shows how the share of people in IPC2 is calculated for a given period.

> _퐼푃퐶푐,푑_ is the IPC phase of district _푑_ in country _푐_ . _푝표푝푐,푑_ is the population in district _푑_ of country _푐_ and _푝표푝푐_ =<sup>�</sup> _푑_<sup>_푝표푝_</sup> _푐,푑_<sup>.</sup> I( _푐표푛푑_ ) is an indicator function, which takes on the value 1 if the condition _푐표푛푑_ is satisfied and 0 otherwise.

5
traps. In this respect, our recommendation is that existing longitudinal survey systems, including those supported by the World Bank LSMS, should be sustained to span longer-term panels, and similar systems should be set up and maintained in countries and regions that lack them and that also face increasing scope, intensity, and frequency of climatic shocks.

Lastly, climate migration is and will always be inherently context-dependent: the evidence we have provided for Nigeria has limited external validity. Replicating this kind of analysis on more countries—ideally using harmonized cross-country migration data—appears essential for broadening the scope of our knowledge on this multifaceted phenomenon.

30
terms of observable characteristics. The results of the RAIPW are presented in Table 5.<sup>8</sup> A higher intensity of CBT sessions is not associated with statistically different impacts on socioemotional well-being or math test scores. Effects remain positive, but not statistically significant when we look at the PLANEA score, there is no clear pattern for the BarOn inventory, but all coefficients are not statistically significant.

**Table 4: ATE on Outcome Measures**

||(1)<br>PLANEA|(2)<br>BarOn|(3)<br>Math<br>score|(4)<br>Enrolled in<br>11th grade|(5)<br>Re-take<br>COMIPEMS|(6)<br>Student<br>Engagement|
|---|---|---|---|---|---|---|
|0 sessions|0.097<br>(0.077)|0.001<br>(0.065)|0.029<br>(0.139)|-0.090***<br>(0.016)|0.025**<br>(0.010)|-0.065***<br>(0.016)|
|1 to 4 sessions|0.143|-0.106|-0.061|0.074***|-0.038***|0.026|
||(0.088)|(0.073)|(0.154)|(0.025)|(0.014)|(0.024)|
|5 or more<br>sessions|0.148|0.062|0.001|0.190***|-0.068**
decisions. Hence, we now look at how important public infrastructure quality is for Brazil’s firms and whether the degree of spatial heterogeneity in quality shown in Figures 2 and 3 is a relevant factor for them. For this, we rely on the World Bank’s Enterprise Survey for Brazil in 2007, which is the closest to our benchmark scenario of 2010.

Table 2 shows descriptive statistics for selected variables in the Enterprise Survey for Brazil by firm size. Overall, 45.6% of firms experienced power outages over the fiscal year 2007, and small, medium, and large firms experienced them in a similar proportion. When asked how much electricity is an obstacle, 45.9% of firms identify it as a major or very severe one. For transport, this number equals 27.5%, but still, 31.8% find it a moderate obstacle. The survey also asks, if the establishment were to relocate, how much would roads and energy reliability be part of the decision? 70.8% and 76.0% of the firms find roads and energy reliability to be at least important for this decision, respectively. Finally, we see no significant differences across firm sizes when asked about the number and duration of power interruptions. These numbers suggest that (i) the quality of infrastructure matters for the firm’s location decision and (ii) firm size alone is insufficient to avoid power outages or obstacles derived from transport.

# **3 A Spatial Equilibrium Model with Endogenous Complementary Infrastructure Networks**

## **3.1 Model Outline**

This section outlines a spatial multisector equilibrium model with endogenous electric power and road transportation infrastructure networks. The model builds on the quantitative spatial modeling literature (Redding and Rossi-Hansberg, 2017) and is closely related to frameworks of Fajgelbaum and Schaal (2020) and Santamaria (2020), where the government chooses the quality of infrastructure to maximize social welfare. Our model extends these earlier studies by incorporating an additional infrastructure
# 1 Introduction

In the three decades prior to the global nancial crisis, household income inequality increased in a large majority of OECD countries. Emerging countries have income inequality signi- cantly higher than the OECD average, although some countries (such as Brazil, Indonesia and, on some indicators, Argentina) have recorded signicant progress in reducing inequality over the past 20 years (OECD, 2011).

In addition, during the late 20<sup>_th_</sup> century there was a general increase in the informal economy in many countries around the world (see Schneider and Enste (2000)). Heintz and Pollin (2005), for example, show that within a data set of 23 countries, 19 showed increases in informality. Similarly, ILO data show that from 2002 self-employment increased in all developing regions, and world-wide it increased from about one-quarter to one-third of nonagricultural employment during 1980-2000 (ILO, 2013). The high incidence of informality is an issue of concern especially in developing countries, where on average, more than 50 percent of the labor force is informal.<sup>1</sup> Among the drivers of rising informality, the conventional view blames trade liberalization for being responsible for the fall in wages for unskilled and low-income workers, as well as the rise in informal and less protected forms of employment. Therefore, despite its uncontroversial expansionary eects on global growth, trade expansion has not always been translated into more equal incomes and better working conditions.<sup>2</sup> In these circumstances, an ecient tax system is indeed an important tool for addressing rising inequality and informality and restoring robust economic growth. On the one side, taxation is a powerful policy tool to redistribute income and make the post-tax income distribution less unequal. On the other side, taxation is a potential tool to lessen the costs of operating in the formal sector, since formality choices are very elastic to marginal tax rates. Indeed, a targeted and well-balanced tax code is
, ownership of mobile phones, and access to electricity. We control for these individual, household and regional characteristics in the regressions below.

On agricultural land ownership and rights, both surveys allow us to compute _individual-level_ indicators related to (i) reported ownership, (ii) economic ownership, (iii) right to bequeath, and (iv) right to sell, in a way that **aggregates** the information reported at the parcel-level. In other words, in the **IHPS** , a self-reporting adult household member is tagged as a reported owner if <u>he/she reported himself/herself as a reported owner for at least 1 agricultural parcel. In the case of</u> number of months living away from the household over the past year; and (iii) household covariates, including household size, dependency ratio, and wealth index. The latter is a factor analysis-based index that is composed of (i) a series of dichotomous variables that capture the ownership of mortar, bed, table, chair, fan, air conditioner, radio, radio with flash drive/micro CD, TV, VCR, sewing machine, kerosene/paraffin stove, electric/gas stove, refrigerator, washing machine, bicycle, motorcycle, car, minibus, lorry, beer-brewing drum, sofa, coffee table, cupboard/drawers, lantern, desk, clock, iron, computer, satellite dish, solar panel and generator, and (ii) a series of dwelling covariates, including number of dwelling rooms per capita and categorical variables that identify construction material (permanent; semi-permanent; traditional); roof type (grass; iron sheets; clay tiles/concrete/plastic sheeting/other); floor type (sand; smoothed mud; smooth cement/wood/tile/other); water source (piped/well; borehole; other), and toilet facility (flush/VIP toilet; traditional latrine; other/none).

11
sex, women have, on average, lower levels of education than men, which also reflects the lower educational level among migrant women than migrant men (Figure 17). Most migrant women have lower secondary education (24.9 percent), while most migrant men have tertiary education (25.9 percent).

Figure 14. Distribution of migrant and non-migrant women by marital status <!-- Start of picture text --> Married Cohabitating<br>Divorced/Separated Widow<br>Single<br>100.0<br>30.1 31.0<br>80.0<br>4.1 7.5<br>60.0 9.9<br>9.6<br>40.0 29.7 17.5<br>20.0<br>34.4<br>26.1<br>0.0<br>Women who migrated Women who did not<br>migrate<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020.

Figure 15. Distribution of migrant and non-migrant men by marital status <!-- Start of picture text --> Married Cohabitating<br>Divorced/Separated Widow<br>Single<br>100.0<br>80.0 36.5 36.9<br>60.0 5.8 5.3<br>18.5<br>40.0 28.3<br>20.0 36.9<br>28.3<br>0.0<br>Men who migrated Men who did not<br>migrate<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020.

Figure 16. Distribution of migrant and nonmigrant women by education levels <!-- Start of picture text --> Non-Migrants
representative at the national and regional levels.

One of the implications of the HBS sample being based on the 2004 Population Census, and not on the 2014 Population Census (the new sampling frame, taken from the 2014 Population Census, will start being used from the 2019 HBS round onward), is that there are some discrepancies between the weighted population distribution in the HBS, and the population distribution from the 2014 Census. In addition to the difference in the estimate of the population total between the two data sources, the distribution of the population across regions also differs between the HBS and the 2014 Population Census, with a somewhat higher share of the population in the HBS being from Chisinau, and relatively smaller shares from the other statistical regions for the same year (Figure 6). These differences stem primarily from the fact that Moldova’s demographics have been affected by significant migration flows, which complicates capturing population dynamics during the ten-year intercensal periods. It is difficult to speculate on the precise ways in which the CEQ analysis in particular may be affected by the sample discrepancies, and an update of the analysis based on HBS data from 2019, based on the new sample frame, would be instructive in this regard.

15
data from the World Bank’s high-frequency phone surveys (HFPS), complemented by simulations using official household surveys conducted before the pandemic.

To identify the long-term threats to inequality and social mobility, one is required to speculate, based on observable patterns in how the immediate impacts of the pandemic were distributed among households, on how the economic recovery is shaping up among households, and how the short-term impacts on children’s education can affect human capital development. Simulations can be used to extrapolate how learning losses due to the pandemic pose a risk to social mobility of the current generation of children. The objective of the forward-looking simulations and extrapolations in this paper is not to predict the future. Rather, it is to identify the sources of risk of long-term inequality and quantify the potential impacts, so that the necessary policy packages can be designed and employed to prevent the risks from materializing.

As noted earlier in the introduction, the conclusions one may draw about the distributional impacts of the pandemic will depend on the types of welfare impacts and inequalities that are being considered. In this paper, we make inference on the welfare impacts by looking at distributional patterns for several indicators, including (i) job losses; (ii) income losses; (iii) food insecurity; and (iv) learning losses. This is primarily driven by the types of questions that are available in the HFPS data. Likewise, since HFPS data do not have detailed income or consumption modules that allow us to compute income or consumption deciles, we rely on data on educational attainment, area of residence, household size and types of jobs to identify households with characteristics that would commonly be more prevalent at the bottom of the income distribution, in order to assess the distributional implications of the pandemic and early recovery.

The remainder of the paper is structured as follows. Section 2 provides an overview of the existing evidence and simulations on the distributional impacts of the COVID-19 pandemic and a conceptual framework that draws out the implications for inequality of the initial impacts and
parametric cross-entropy methods, but keeping it consistent with the United Nations’ population projections and levels of education observed in the household surveys.<sup>11</sup> To be consistent with the GIDD, LINKAGE is modified to adopt the former’s skilled-unskilled labor definition, whereby a skilled worker is anybody with more than nine years of education, and an unskilled worker is anybody with nine years of education, or less. This redefinition necessitates an adjustment of the GTAP data on value added by labor type in production, such that the number of workers of a given skill type in a given sector is consistent in the 2007 benchmark year across the two modeling frameworks.

# **3.2 Scenarios**

Using the simulation framework described above, seven alternative future scenarios are considered. The scenarios are designed so as to be able to identify the marginal impact of a given policy outcome as it interfaces with demographic change (Table 1). The policy outcomes are those related to the demographic dividend and include the maintenance and improvement of employment ratios, improvements in educational attainment and quality, and changes in savings behavior. To achieve this, first a “business-as-usual” scenario is considered to establish a baseline economic path till 2050 for the SACU economies. In the baseline, educational attainment rates are assumed to be constant, the total labor supply grows at the same rate as the working-age population (based on the same rate as the UN WPP’s medium fertility scenario), and employment ratios remain constant at 2015 values. The current account balances and investment shares track the forecasts of World Bank (2015b) till 2017 before converging to longrun values. Labor productivity is assumed to grow in a fashion that real GDP grows till 2017 following the World Bank (2015b), and following growth projections under the Shared-SocioEconomic Pathway 2 scenario of Dellink et al. (2015) from 2018 to 2050.

The alternative scenarios then deviate from this baseline scenario in a specific, but different, way. The differences in economic outcomes
<br>0.00<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Operations subsidies Government equity injections<br>Deferred tax liabilities Government loans<br>SOE loans<br>Fiscal injections as percent of GDP<br><!-- End of picture text --> _Source_ : Authors, based on data from the World Bank Infrastructure SOE Database.

19
ASSET, SALE, EBIT, and CHAEBOL to be positive, as they should increase the probability of foreign investment. If foreigners buy only the better firms then the coefficient on FDEBT should be negative. Alternatively, over-indebted firms may be more likely to be vulnerable to takeover, suggesting the coefficient might be positive. We also include (2-digit SIC) industry fixed effects in all regressions, The first three columns of Table 4A report the coefficient estimates from this regression equation. They were estimated using both Logit and Probit. The results are very similar in both cases so we report only the Logit estimates. All three variables-size, profitability, and chaebol membershipincrease the likelihood of foreign investment. EBIT and DEBT are negatively correlated (the simple correlation coefficient is -0.37). When debt is not included the coefficient on profitability is positive and highly significant. Firms with greater debt are significantly less likely to receive substantial foreign investment, suggesting that highly indebted firms, although perhaps in greater need of foreign capital, do not attract foreign ownership.<sup>8</sup> Next, we look for evidence on capital shortages using data on chaebol affiliation. We include business group data since domestic investment in Korea often comes via the firm's group affiliation, as most chaebol have affiliated banks and non-bank financial companies. If groups have limited access to capital, then we expect the group debt variable to positively impact foreign ownership of their affiliates. We include the log of group assets (GASSET) and group debt-asset ratios (GDEBT). We expect group assets to negatively impact foreign ownership since these groups can subsidize firms more easily.

8Kang and Stulz (1997) evaluate the effect of firm characteristics on foreign ownership in Japan and find similar results for firm size and leverage. They interpret this as evidence that foreign investors do not hold the market portfolio. Our results suggest that some of the relation that they are picking up is because ownership data also contains merger and acquisition activity, which should not be expected to mirror
**Figure A1.** Comparing skills measures from Philippines STEP survey and O*Net database

<!-- Start of picture text --> Non-routine analytical Non-routine interpersonal Non-routine manual<br>2.0 2.0 1.5<br>1.5 1.5<br>s = 0.75 1.0<br>s = 0.57 s = 0.67<br>1.0 1.0<br>0.5<br>0.5 0.5<br>0.0 0.0<br>0.0<br>-2.0 -1.0 0.0 1.0 2.0 -2.0 -1.0 0.0 1.0 2.0<br>-2.0 -1.0 0.0 1.0 2.0<br>-0.5 -0.5<br>-0.5<br>-1.0 -1.0<br>-1.5 -1.5 -1.0<br>Philippines STEP survey Philippines STEP survey Philippines STEP survey<br>Routine manual<br>Routine cognitive<br>2.0 1.5<br>1.0<br>1.5<br>0.5<br>1.0 s = 0.49<br>0.0<br>0.5 -2.0 -1.0 0.0 1.0 2.0<br>-0.5<br>0.0<br>-1.0<br>-2.0 -1.0 0.0 1.0 2.0<br>s = -0.77<br>-0.5<br>-1.5<br>-1.0 -2.0
in developing countries. This insight is essential for understanding the victimization of vulnerable populations and for

> 6Comino et al. (2020) examines the effects of the 1986 migratory amnesty, J ́acome (2022) the effects of the 2015 Priority Enforcement Program, and Pearson (2024) the effects of the 2012 Deferred Action for Early Childhood Arrivals. Another close contribution is Xie and Baumer (2021), who offer a descriptive study using data from the National Crime Victimization Survey.

6
|9.6|36|38.4|
|General cargo vessel<br>turnaround time|Hours||48|60|2.2|48|40.8|45.6|
|Craneproductivity|Containers<br>per hour|─|─|─|18|13|12|─|
|Craneproductivity|Tonnes per<br>hour|23|15|─|16|14|9|8|
|Container cargo<br>handlingcharge|US$ per TEU|220|180|160|260|168|155|─|
|General cargo<br>handlingcharge|US$ per ton|9|9|15|14|10|8|8|
|Bulk dry handling<br>charge (ship to gate or<br>rail)|US$ per ton|5|5|5|5|3|||
|Adequate road access<br>present|0=no;1=yes|0|0|0||0|0|0|
|Landlord model is used|0=no;1=yes|1|0|0|1|1|1|1|

_Source_ : AICD ports database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. _Note_ : Data are as of 2006. — = data not available.

## **Challenges**

The Port of Cotonou is operating under the pressure of growing demand; by 2005 demand was almost twice the port’s designed capacity. Demand for general cargo and dry bulk handling grew 140 percent between 1996 and 2005, from 2.03 million tons handled to 4.84 million (76 percent dry bulk and 24 percent general cargo, figure 9a). The port’s design capacity is 2.3 million tons per year, but as of 2005 it was handling an excess of more than 2.5 million tons per
elasticity of employment with respect to output; and second, the higher the share of capital the larger will be the impact on GDP of any shock to investment. Both implications of the capital-intensive feature of the Egyptian economy have important consequences for the analysis of the impact of the crisis on Egypt as will be seen in the next two sections.

Two other differences with previous growth decomposition studies are the use of a variable rate of return to education through time, as well as a changing participation rate in the last years (Said, 2009). The rising rate of return to education in the decade between 1998 and 2006, as well as the increasing rate of labor participation imply higher rates of human capital accumulation.

> **4** Using social accounting matrices, several studies found that the share of capital in output increased from 68 percent in 1988, to 69 percent in 2001, and to 73 percent in 2007 (Eckaus et al., 1981; Akhter et al, 2001; and Kantor Management, 2009). Yet, the share of labor income might be underestimated because the income of self-employed is imputed as capital income (Gollin, 2002 and Bernanke and Gurkaymak 2001).

> **5** Note that the first study is based on Household Income and Expenditure Surveys (HIES) while the second one is based on Labor Market Surveys.

12
.org/10.1186/s12916-019-1464-9.

- NCD Child. 2023. NCD Global Burden of Disease Dashboard. <u>https://www.ncdchild.org/global-burdens-ofdisease-dashboard/</u>

- Neufeld, Lynnette M, Sheryl Hendriks, and Marta Hugas. 2023. “Healthy Diet: A Definition for the United Nations Food Systems Summit 2021.” _Science and Innovations for Food Systems Transformation_ , 21–30. https://doi.org/10.1007/978-3-031-15703-5_3.

- Ng, Marie, Tom Fleming, Margaret Robinson, Blake Thomson, Nicholas Graetz, Christopher Margono, Erin C Mullany, et al. 2014. “Global, Regional, and National Prevalence of Overweight and Obesity in Children and Adults during 1980–2013: A Systematic Analysis for the Global Burden of Disease Study 2013.” _The Lancet_ 384 (9945): 766–81. https://doi.org/10.1016/S0140-6736(14)60460-8.

- Nguyen, Phuong Hong, Lan Mai Tran, Nga Thu Hoang, Megan Deitchler, Mourad Moursi, and Gilles Bergeron. 2023. “The Global Diet Quality Score Is Associated with Nutrient Adequacy and Depression among Vietnamese Youths.” _Annals of the New York Academy of Sciences_ , August. https://doi.org/10.1111/nyas.15053.

- Nilson, Eduardo Augusto Fernandes, Adriana Blanco Metlzer, Marie Eve Labonté, and Patrícia Constante Jaime. 2020. “Modelling the Effect of Compliance with WHO Salt Recommendations on Cardiovascular Disease Mortality and Costs in Brazil.” _PloS One_ 15 (7): e0235514. https://doi.org/10.1371/journal.pone.0235514.

- Nilson, Eduardo Augusto Fernandes, Everton N. da Silva, and Patrícia C. Jaime. 2020. “Developing and Applying a Costing Tool for Hypertension and Related Cardiovascular Disease: Attributable Costs to Salt/Sodium Consumption.” _The Journal of Clinical Hypertension_ 22 (4):