rafmacalaba/data-use-mentions-extended huggingface dataset:
the suspension of public services (e.g., banking, telecommunication, electricity, and transport services). Specifically, we find that the outbreak of the civil war has dramatically reduced households’ access and ability to buy food while also significantly disrupting livelihoods of households in conflict affected areas. Non-farm and wage related activities appear to be the most affected while farm activities were more resilient than other activities.<sup>8</sup> Similarly, economic activities in urban areas were much more affected than those in rural areas. This is partly because urban areas and main roads connecting towns continued to be heavily militarized even during periods of relative lull in high intensity active war. These suggest that the ultimate impacts of the violent conflict, in addition to curtailment of livelihoods, were likely mediated through disruptions in the functioning of markets and supply chains in urban areas. Tigray has continued to be disconnected from the rest of the country for several months after the last survey round, and it is likely that these impacts have deepened in subsequent months. Our findings support the urgent calls for large-scale humanitarian assistance that were made even at early stages of the conflict.

The rest of the paper is structured as follows. Section 2 describes the geographic and political context of this study and data used. Section 3 summarizes trends in welfare outcomes. Section 4 outlines the empirical strategy while Section 5 presents our results and discussion. In Section 6, we provide concluding remarks as well as general discussion of the value – and shortcomings – of high-frequency phone surveys for monitoring large-scale conflicts in other settings in the future, including the preparatory work that would be required to enable mobilization of such efforts.

# **2. Context and Data**

## **2.1 Context**

For over two decades, Ethiopia has been one of the world’s fastest-growing economies and often nicknamed as the “hub of stability” in a volatile Horn of Africa region.<sup>9</sup> This changed drastically in
care worker wages. Based on US longitudinal data, Hotz and Xiao (2011) find that more stringent regulations on establishment inputs increase quality (particularly in high-income locations) but reduce child care provision (particularly in low-income locations). Bastos and Cristia (2012) develop a model of monopolistic competition to explain the behavior of child care providers in a large Brazilian city. Consistent with their model, they find a positive empirical association between household income and child care quality. We also model the supply side as one with monopolistic competition. We build on the two-stage model of Mazzeo (2002), whose study is known in the industrial organization literature as one of the first to estimate an entry model with endogenous product characteristics (Reiss and Wolak (2007)). This framework has been extended and adapted to study competition among health maintenance organizations (Dranove et al. (2003)), and in the the video retail (Seim (2006)) and airline industries (Ciliberto and Tamer (2009)).

The remainder of the paper is organized as follows. Section 2 describes the model, and Section 3 presents the data. Section 4 discusses econometric issues, including identification and estimation procedure. Section 5 presents the estimation results, and Section 6 reports the policy simulations. Section 7 concludes, and Appendices A–F contain additional materials.

# **2 Model**

The economy includes two sets of agents: households and child care providers. Households consist of one or two parents and one child. Parents are the decision makers, as they choose their own labor supply and the child’s care. They can care for the child fully by themselves, in which case they cannot both work full time. They can also use non-parental child care providers, in which case they have three options. The first is child care centers, operated by private firms. Potential center-based providers decide whether to enter the market or not, and choose quality and price. Their supply is endogenous; in equilibrium, it
Our goal in this paper is not to provide a perfect measure of education quality. Rather, we provide a practical yet rigorous and globally comparable set of estimates with large and inclusive country coverage over time. We hope this dataset can be used to reveal important descriptive trends in human capital formation across developed and developing countries. We also hope to enable analysis of factors correlated with and that have plausible causal links to the formation of human capital and economic growth. Finally, we hope this dataset can be useful for monitoring and evaluation of important policy goals.

Future iterations of this dataset will continue to expand coverage across countries and time as countries join existing assessments and by including additional assessments such as early grade reading and mathematics assessments. Moreover, we aim to build a dataset that enables over-time isolation of value-added learning by including variables which can account for various selection effects. This includes linking quality of education data to quantity of education data, as well as including measures of enrollment and retention across schooling levels. We also aim to enable further identification of the link between education quality and economic growth by including variables such as comparable estimates on the returns to education. We hope this dataset, and future iterations, will enable a deeper understanding of mechanisms driving human capital formation, the link to development, and useful policy applications.

22
2.049<sup>_∗∗_</sup>|
|(1.374)<br>(0.850)|
|_First stage_|
|IV: Treat _×_ Post2007<br>-0.111<sup>_∗∗∗_</sup><br>-0.149<sup>_∗∗∗_</sup>|
|F-stat<br>353.74<br>553.24|
|R-squared<br>0.316<br>0.419|
|Observations<br>20,731<br>20,731|

_Source_ : Iranian Households Income and Expenditures Surveys (HIES). Note: Table presents estimated coefficients of Eq (4) and (5). Dependent variable is Ln(Household Spending on Education). Since education spending and total household expenditure are both in logarithmic form, the estimated coefficient ( _ξ_ ) denotes elasticity. I also use family income itself as a robustness check. Additional controls include household size, head’s age, and head’s education. Heteroskedasticityconsistent standard errors accounting for clustering at the province and industry level in parentheses. P-values are calculated using wild bootstrap randomization inference (WBRI). Treated: Oil and Gas, Energy Supply industries. Synthetic control: Information (0.148), Education (0.169), and Health (0.683) industries.<sup>_∗_</sup> Significant at 10% level;<sup>_∗∗_</sup> significant at 5% level;<sup>_∗∗∗_</sup> significant at 1% level.

This negative effect on education spending is large compared to studies that find the income elasticity of education spending (Acar et al. (2016); Huy (2012); Qian and Smyth (2011)). While the results of these studies are different across countries, levels of family
- In the crisis zone, the economy is vulnerable to a self-fulfilling crisis and the government’s optimal policy is to default only if it could not roll over its stock of debt. Countries that fall in this zone are susceptible to uncertain shocks. These countries might find themselves in a crisis without even knowing that they are at risk when an uncertain shock were to materialize.

- In the default zone, the interest payments are so large that the government would choose to default even if investors were willing to refinance the stock of debt. Countries in the default zone can be considered vulnerable since they are estimated to face an elevated risk of default in the event of a crisis.

To produce outputs, the model requires a set of calibrated parameters. The model requires an estimate of the probability of a panic (proxied by the subjective default rates identified above<sup>10</sup> ); the maturity composition of public debt;<sup>11</sup> the structural levels of revenues and expenditures; output loss upon a default; and the average yield to maturity.

As an example Cole and Kehoe (1996) illustrates the usefulness of these models in the case of Mexico. Mexico was not facing imminent default right before the 1994-1995 crisis. Yet, public debt was high and the share of short-term debt was high. Mexico had debt levels that were in the crisis zone due to **short maturity bonds** . In 1994, there was political turmoil followed by the assassination of the ruling party’s presidential candidate Luis Donaldo Colosia. Subsequently, Mexico defaulted. Mexico could have been better prepared to address a potential shock by increasing its maturity structure and issuing bonds in local currency. Also, the government might have pursued fiscal consolidation more forcefully if there had been more awareness about the risk of default (Cole and Kehoe, 1996). The Theory index would then have picked up if Mexico was getting close to the default zone and would have coded it as a value of
20 level, as well as at the level of individual states.<sup>23</sup> A recent study applies the small area estimation methodology used here to estimate poverty at the local level in three states of India in 2004/5 (Gangopadhyay et al, 2010). The study confirms that in West Bengal, Orissa and Andhra Pradesh the poverty-city size gradient observed from NSS data also emerges from estimates derived out of the small-area estimation procedure (Table 10). Thus, at least in India, the finding of a poverty-city size gradient is robust to alternative empirical methods. This provides some support to the claim that the findings reported in preceding sections are not driven by our reliance on small-area estimation techniques.

# **8. Conclusions**

Using highly disaggregated poverty map data from eight countries drawn from all six regions of the developing world, we have shown evidence of a common – although not universal – inverse relationship between poverty and city size. In all countries in our sample, poverty is both more widespread (higher FGT(0)) and deeper (higher FGT(1)) in very small and small towns (those with a population below 100,000) than in large or very large cities (those with a population greater than 0.5 million). Metropolitan poverty, in particular, is considerably lower than poverty in other urban areas in all countries in our sample, except for Kenya. Dominance analysis of cumulative distribution functions indicates that the basic pattern is generally robust to the choice of poverty line.

Neither is it true that, because of sheer population size, most poor people in these countries live in large cities. In fact, in all eight countries, a majority of the urban poor live in medium, small or very small towns. In four of the eight (Albania, Brazil, Sri Lanka and Thailand), a majority of the urban poor live in towns smaller than 100,000 people.

The greater incidence and severity of consumption poverty in
the average expected expenditures of current consumers in the same group is denoted by gg,ii ww ∂ .15 If the estimated proportion of entrants in group ii gg is uu ∂∂cc∂ cc denoted by (estimated using a probabilistic model), then the following is true: gg,ii uu gg gg,ii ππ ∂ ∂ (10) ww ∂∂ccnn∂ nn gg,ii gg,ii gg,ii uu = ππ uu

> 12 See the mcema user manual for more information (Rodríguez-Castelán et al. 2021). A detailed discussion on the implications of the availability of price data on the items of interest is also presented in the manual.

> 13 The mcema module for Stata allows a selection among six methods to estimate the expenditure of new entrants: (a) imputation using average expenditures (unconditional mean imputation), (b) random imputation using the expenditures of current users, (c) imputation based on expenditure models and linear regressions (regression, conditional mean imputation), (d) imputation using an expenditure model and the random imputation of residuals (stochastic regression imputation), (e) imputation based on an expenditure model and quantile regressions, and (f) imputation using a quantile regression model and a random imputation of residuals.

> 14 Jackson (1984) introduces the foundations of the hierarchic demand system whereby the change in a bundle of goods can be modeled. In what follows, a selective demand model is introduced to link the selection of goods with real income. There are two hot deck approaches. The first is the distance function approach, and the second is the pattern matching approach. The first imputes the missing value based on the smallest squared distance statistic to the case with the missing value. The matching pattern method used here assumes that the sample can be stratified into separate homogenous groups and that imputation can be performed randomly within each group (Fox-Wasylyshyn and El-Masri 2005). See also Ahmad and Stern (1984, 1991); Araar (1998)
Policy Research Working Paper 10484

# **Abstract**

The COVID-19 pandemic significantly changed mobility patterns in the Bogotá and Buenos Aires metropolitan areas, as shown by the differences between the October 2019, 2020, and 2021 indicator values derived from call detail record–based origin-destination matrices. The differences between 2019 and 2020 were more notable than between 2019 and 2021 on most mobility indicators, demonstrating a reversal of the pre-pandemic mobility habits. However, by late 2021, the return to pre-pandemic levels was still very partial in the case of public transport use (especially so in Buenos Aires), while in Bogotá the pandemic appeared to have induced a permanent—and increasing—shift to nonmotorized modes. Other mobility indicators that appear to have changed more permanently in Bogotá include the lower average distances traveled and the relatively higher importance of non-home-based mobility. In the Buenos Aires Metropolitan Area, the key persistent changes include the lower overall trip generation rates and specifically peakhour travel, and the higher relative weight of travel to work and school compared to other travel purposes. These findings are partly explained by the underlying policy and regulatory context in the two cities and are relevant for designing transport policy in the post-pandemic context, including in terms of public transport route and schedule planning, cycleway network expansion, and, more broadly, the leveraging of big data as a complement to traditional mobility surveys.

This paper is a product of the Transport 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 astokenberga@worldbank.org, eivarsson@worldbank.org, and jfulponi@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,
*|**($ iion)**|**( miim**<br>**o%)**||
|**Europe**|121|88.5|10,707|10,707|100|
|CIS|64|76.8|4,937|2,855|58|
|**Domestic**|301|47|11,536|1,730|15|
|**T tal**|t<br>**4860**0||**20T067,1**<br>n|**80**<br>**15,292**|**56**|

_Source:_ Company data, Customs Committee, Brunswick Warburg, Morgan-Stanley Dean Witter Research, staff calculations From the table, one can calculate that even though domestic shipments exceeded 60 percent of total gas deliveries in 1997, they accounted for only 42 percent of sales, and a mere 11 percent of total cash receipts. Table 7 shows that the average annual implicit subsidy passed on by Gazprom to domestic customers as a result of unrecoverable arrears and inflated NCS prices was about 1.5 percent of GDP per year over 1993-1997, a cumulative total of $26.2 billion.<sup>24</sup> Electric power utilities account for approximately 50 percent of Gazprom's overdue receivables, followed by chemical industry - 7 percent, and ferrous metallurgy - 5 percent.

> 23 We do not include tax offsets as these do not have a direct debt impact (only through pre-emptive tax arrears), although they constitute a subsidy and adversely affect the quality of public spending.

> 24 Pinto, Drebentsov and Morozov (2000). Note that with regulated gas and electricity prices, the energy monopolies cannot in turn inflate their prices for in-kind settlements. Note further that gas prices in Russia are a fraction of those in central Europe, where western prices
**Table 2: Chinn-Ito Index of capital account liberalization for selected groupings**

||**1990–1994**|**1995–1999**|**2000–2004**|**2005–2009**|
|---|---|---|---|---|
|**Core Countries**|83.2|96.1|97.4|100.0|
|**Non-Core Countries**|19.5|80.5|96.6|100.0|
|**Other Euro Countries**|-37.9|-9.5|24.7|81.3|
|**Non-Euro Countries**|8.8|39.0|66.9|87.9|

Note: The Chinn-Ito Index is expressed in terms of its highest value for all countries considered in their sample. Thus, a value of 100 means complete liberalization. Core countries include Austria, Belgium, France, Germany, and the Netherlands. Non-Core countries include Greece, Ireland, Italy, Portugal, and Spain. Other Euro countries include Estonia, Malta, the Slovak Republic, and Slovenia. Non-Euro countries are comprised of Sweden and the United Kingdom. Source: Chinn and Ito (2008).

8. Financial integration resulted in a sharp increase in cross-border capital flows with claims by core countries on non-core countries‘ banking systems dominating the increase. Lending by German and French banks to non-core countries‘ banks was by far the most important factor in this increase.

**Figure 2: Core bank claims in Non-Core countries**

<!-- Start of picture text --> US$ billions<br>2500<br>Dutch banks<br>2000<br>German banks<br>French banks<br>1500<br>Belgian banks<br>1000<br>500<br
**|10.75|10.63||**_IGME6 _**|0.257|0.36|
|**_ES9_**|11.66|11.71||**_IGME9 _**|0.333|0.36|
|**_ES16_**|14.71|14.23||**_IGME16_**|0.51|0.36|

Notes: (1) The data used are CFPS 2016 with first-born children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; standard errors clustered at province-level in square brackets; *** p<0.01, ** p<0.05, * p<0.1.
# **2 Argentina**

Argentina is one of the wealthiest countries in Latin America and is well positioned in terms of educational indicators. In 2000 (the year of the data used in this paper), per capita GDP was around PPP$ 10 _,_ 000, the literacy rate was nearly 98%, primary net enrollment was close to 100%, and secondary school enrolment was just above 80%.<sup>1</sup> However, as in much of Latin America, wealth was distributed extremely unequally and poverty was far from eradicated. According to official figures, the Gini index of per capita household income circa 2000 was 0.50, 14% of people lived below the 2.5-dollar-a-day poverty line, and 29% below the national poverty line.

In the last couple of decades, the composition of primary school pupils in Argentina has changed (Llach 2006). Enrolment into primary schooling has risen significantly since 1980, particularly among less well-off students, and drop-out rates have fallen appreciably (Rivas 2010b). Along with these changes, the quality of education received is much less equal. The difference in achievement between private and public school pupils has broadened in the areas of the country with weak technical capacities following the school decentralization process in the early nineties (Galiani, Gertler & Schargrodsky 2008), and the inequality of achievement remains sizeable in the poorest provinces (Etchart, Gasparini, Bohorquez, Curia, Ferroni & Hontakly 2004).<sup>2</sup> Disparities in test scores are driven by differences at three levels: the school, the class, and the pupil. Schools differ in their physical, human and ‘social’ resources. Spending per student may greatly vary across schools. As an example, the private sector in Buenos Aires province spends 30 percent more per pupil than the public sector (Rivas 2010b). There are also large disparities between spending in the public sector across provinces – for instance, in 2009 Tierra del Fuego public schools
DIALOGIC READING AND STORYBOOK EVALUATION IN KENYA 20 study (Fernald, Prado, Kariger, & Raikes, 2017). To minimize the testing burden, we extensively pretested all items and ordered them by difficulty using item response theory, with a final assessment of 27 Luo items (Cronbach’s alpha: _�_ = 0.78, Cohen’s Kappa for IRR: _�_ = 1), 32 Swahili items ( _�_ = 0.76, _�_ = 0.89), and 34 English items ( _�_ = 0.57, _�_ = 0.95) that demonstrated variability among children in our study area and age range (author manuscript, under review). While this adaptation decreased the comparability of our vocabulary scores to other studies or populations, we were primarily concerned with developing measures that would be appropriate for the multilingual study environment.

In collaboration with local artists, we also developed a locally-appropriate measure of expressive vocabulary after pre-testing suggested that the stimulus words and pictures in existing measures of expressive language were not appropriate to our context (author manuscript, under review). The measure was based on words from the BPVS, ordered by difficulty. This 30-item measure of expressive vocabulary was administered by having the assessor show a picture and ask the respondent to name the object or concept the illustration depicted. Each item was scored as 0 or 1, with credit received for responding in English, Luo, or Swahili ( _�_ =0.66, _�_ =0.95). To assess children’s knowledge of the vocabulary words embedded in the storybooks, we added seven expressive vocabulary items from the project storybooks (crayon, goose, kite, nest, rabbit, toothbrush, and umbrella). For all vocabulary outcomes, age-normalized z-scores were constructed by estimating the relationship between age and the outcome variable nonparametrically (Fan, 1993).

Child characteristics measured at baseline included age (in months), sex, and height
in 2006, and 94.6 percent in 2008. The percentage of women who tested positive was 2.9 percent in 2004, 3.1 percent in 2006, and 3.7 percent in 2008.

Figure 1 depicts the distribution of the likelihood women assigned to being HIV-infected in the 2006 and 2008 rounds (pooled), measured by beans on a scale of zero to ten. Fifty percent of the reports were of zero beans. The percentage of women who chose each category decreases with the number of beans, except for the 5-bean category. Fewer than one percent chose the 10-bean category. Table 3 shows the average number of beans. The overall sample average is 1.52 beans. The average number of beans chosen in each region conforms to the ranking of HIV prevalence in the general MDICP sample. The averages are 1.78 beans in Balaka, 1.69 in Mchinji, and 1.16 in Rumphi. The 2004 HIV prevalence rates for these regions are 7.9 percent in Balaka, 6.4 percent in Mchinji, and 4.4 percent in Rumphi. The table also shows that average beliefs decrease with schooling and are higher for women married to a polygamous husband.

Figure 2 depicts the distribution of the likelihood women assigned to being HIV-infected after receiving HIV test results. Because the testing component of the survey was conducted after the interview components, these are beliefs reported two years after the tests (the subsequent round of data collection). Because of low HIV prevalence and high attrition of infected women, there are only 18 women who report their beliefs two years after receiving a positive test result. The average number of beans allocated by these women is 4.67 beans, which is signicantly higher then the average of 1.5 beans reported by women who received a negative test result. Most of the women who received a positive test result assign at least some likelihood to not being HIV infected.

I obtain information about births from family rosters that were collected in 2006