rafmacalaba/data-use-mentions-extended huggingface dataset:
an average yearly GDP per capita over the period 2003-2017 equal to or greater than the 75% of the sample average, but lower than the 90% of the sample average. Less developed regions are those with an average yearly GDP per capita over the period 2003-2017 equal to or greater than the 50% of the sample average, but lower than the 75% of the sample average. Low-income regions are those with an average yearly GDP per capita over the period 2003-2017 lower than the 50% of the sample average. High- (not high-)income regions are those with an average yearly GDP per capita over the period 2003-2017 equal to or greater than (lower than) the 90% of the sample average. High- (low-)growth regions are those recording a long-run growth rate of GDP per capita between the years 2003 and 2017 equal to or greater than (lower than) the 50% of the sample average. Decline of GDP per capita around the Great Recession is defined by comparing the average yearly GDP per capita over the pre-crisis period 2003-2007 with the average yearly GDP per capita over the subsequent period 2008-2017.

**The varied performance of NUTS-3 regions is shown particularly in their trajectories after the Great Recession.** We examine the performance of NUTS-3 regions in the pre-crisis period 2003-2007 and the subsequent period 2008-2017, to find the proportion of regions that experienced a decline in the average yearly GDP per capita between the two periods. According to data summarized in Table 8, the share of ‘declining’ NUTS-3 regions is 20.4% among ‘high-income’ regions, 28.9% among ‘transition’ regions, 27.3% among ‘less developed’ regions, and only 2.8% among ‘low-income’ regions.<sup>29</sup> Such a pattern emerges clearly from Figure 6, which plots the average GDP per capita among each category of NUTS-3 region.  On average, ‘low-income’ regions have recorded a better trend than regions in
Longitudinal research in the United States indicates that age of entry into kindergarten shows a modest relationship to cognitive-academic achievement but not social-emotional functioning (NICHD Early Child Care Research Network, 2007), and researchers note that age-of-entry effects are dwarfed by other aspects of children’s family and early care and education experiences.

## Current Study

In order to better understand the developmental status of young children in Pakistan, and the impact of various risks on young children’s development, we conducted a nationally representative telephone survey of households with children aged 0-6 years. Health protocols precluded in-person interviews during the pandemic. To date, a nationally representative study of child development among children under age 3 or school readiness for those ages 3-6 has not been conducted in Pakistan. Thus, these data fill critical knowledge gaps.

This study shares the findings from the survey, conducted from December 2021 to February 2022. The aims of this study are to describe the current status of ECD and school readiness for children aged 0-6 in Pakistan and explore how these outcomes are related to six key risk factors—food insecurity, living in a rural area, low maternal education, lack of psychosocial stimulation at home, experiences of parental distress, and no ECE enrollment. This list of risks a child could face is not intended to be exhaustive. These are risks informed by the literature and identifiable in the current survey format.

This study aims to fill key gaps in the literature for ECD particularly around parenting capacity as related to parents’ education levels, mental health or distress levels, and engagement with their young children, specifically provision of psychosocial stimulation. The study also provides insight into the state of child development and subsequent risk factors during the COVID-19 pandemic. Section 2 discusses the data and methods and describes both the research design as well as key variables. Section 3 reports results and section 4 concludes with a discussion of the implications for policy and programming.

# **2. Data and Methods**

## Research Design and Data Collection Strategy

Gallup Pakistan
# extent across countries.

Globally, the evidence on disability prevalence and on disability gaps in education, employment, living conditions and more has grown in recent decades. Regarding prevalence, as part of the Global Burden of Disease (GBD) study (Murray and Lopez 1996), disability prevalence is regularly estimated: it is inferred from data on health conditions and impairments alone using assumptions on distributions of limitations that may result from health conditions and impairments. The World Health Survey has also been used to develop disability prevalence estimates based on core functional domains (seeing, concentrating, moving around, self-care) (Mitra and Sambamoorthi 2014) or based on an index with information on eight domains (vision, mobility, cognition, self-care, pain, interpersonal relationships, sleep and energy, affect) (WHO- World Bank 2011).

Regarding disability gaps in well-being outcomes, while most of the evidence a decade ago pertained to high income countries (HICs), there has been growing evidence that persons with disabilities in LMICs are disproportionately more likely to experience multidimensional poverty, i.e. experience multiple deprivations, are less likely to work and to have ever attended school and more likely to experience morbidity (e.g. Banks et al 2017; Mitra 2018; Pinilla-Roncancio et al 2020; United Nations 2019). For instance, in a systematic review on poverty and disability in LMICs, Banks et al 2017 find that <mark>81% of studies found evidence of a positive association between disability and a poverty marker. This relationship persisted when results were disaggregated by gender, measure of poverty used and impairment types. By age group, the proportion of studies reporting a positive association between disability and poverty was lowest for older adults and highest for working-age adults (69% vs. 86%).</mark> However, deriving any definitive conclusion on inequalities across disability status remains problematic in this literature that uses varying measures for disability, well-being indicators, data sources, and methodologies. Some studies measure disability through broad
# **1. Introduction**

In Latin America, as in large parts of the developing world, aggregate indicators show that access to infrastructure services is still largely rationed, with both households and firms suffering from shortfalls across all major types of infrastructure (Estache and Fay 2009). This situation is likely to have been exacerbated over the last two decades by the rapid growth in the share of households attaining middle-income status and the growth in demand for infrastructure services they generated (Ferreira et al. 2013, Lakner and Milanovic 2015). However, despite its potential importance in designing infrastructure policy, there is no systematic information on how access to infrastructure varies across the income distribution.

This paper addresses this gap by documenting access to services and ownership of infrastructure-related durables in the water, energy, telecom, and transport areas, based on large scale harmonized household survey data from Latin America covering 14 countries over the 1992 to 2012 period. It provides a systematic disaggregation of access and ownership rates across the income distribution, allowing for an assessment of the situation of households at different levels of income and over time across the countries of the region.

For a number of indicators, the data reveal wide disparities across households at similar levels of income in different countries. We then econometrically derive country infrastructure premia, which show how much a household at a specific level of the income distribution benefits from simply being located in a given country, and assess the progressivity or regressivity of infrastructure access patterns across the income distribution. We then show that these premia are only partly explained by per capita GDP and are not systematically aligned with other country overall characteristics such as inequality, density, or urbanization, which means they are very likely to be for a large part policy driven.

Second, we compute the income elasticity of infrastructure access for our set of indicators, and use these to forecast the time that would be needed to close the remaining gap for households at different levels of the income distribution under a business as usual hypothesis. The general
such as land area or respondent characteristics, are correlated with harvest mis-reporting, so even in cases of short recall periods self-reported harvest quantities may well be inaccurate.

Resolving this question systematically would require comparing farmer-reported harvest quantities to more objective benchmark estimates, for example based on crop cutting. Crop cutting is not available in the three main data sets used for this analysis. As an ancillary analysis, we use the MAPSII data set to benchmark the farmer self-reported harvest against the more objective full-plot crop cuts. We replicate the main specification for the effect of recall length on farmer-reported harvest in the sample of plots for which a full-plot crop cut is available. Then, we quantify the reporting error (farmer-reported harvest quantity minus full plot crop cut quantity) and assess the effect of the recall length on the reporting error. We find a significant effect of the recall length on self-reported harvest as well as on reporting error (Figure 7). This suggests that farmers are indeed increasingly over-reporting harvest with longer recall periods, at least in this sample.

# Labor

Reported farm labor per plot increases significantly with recall length. The effect size is similar in Tanzania NPS 2012/13 and Malawi IHS4 at 1.6 (2.3 percent) and 1.2 person-days (2.8 percent) per plot per month of recall length, respectively (Figure 8, Figure 9, Table A.9, Table A.10, Table A.11).<sup>9</sup> A quadratic rather than linear specification for recall length suggests no mis-specification by the linear model, and the BIC’ analysis shows the linear model provides a better fit in both data sets (results not shown).

> 9 The comparison with the Malawi IHS4 panel is omitted because in that survey labor inputs are collected partially in the first visit (labor used for land preparation and planting, ridging and fertilizing) and partially in the second visit (harvest and post-harvest labor). Thus, there is no one unique recall period against
_j_ in year _t_ . FDA=1 if product _p_ requires submission to FDA for prior notice to be imported into the United States. Coefficients on spatial exporting controls are not reported for brevity. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

# **5.3 Alternative measure of depth**

We use another measure of depth based on Mario Larch’s Regional Trade Agreements Database from Egger and Larch (2008). Without focusing on individual provisions, this data set classifies RTAs into three broad categories —- partial scope agreements (PS), free trade agreements (FTA), and customs unions (CU) —- that are increasingly more comprehensive.<sup>30</sup> Table 9 provides additional supporting evidence that deep RTAs generate positive spillover effects on third-country exporters. Columns (1) and (4)

> 29This ranges from 0 to 156, with the maximum being for 0406 cheese and curd. Results are reported in Appendix Table A.7.

> 30FTA and CU are as defined in Paragraphs 8(b) and 8(a), respectively, of Article XXIV of GATT 1994, and a partial scope agreement means that the agreement covers only certain products. For more details on the database, see https://www.ewf.uni-bayreuth.de/en/research/RTA-data/index.html.

18
ideally uses firm level data.

Furthermore, trade-related reforms require consideration of the complementarities between the various policy constraints. Building multilane highways will not raise trade if trucks must wait at the border. Port improvements would have limited impact if the problem is getting goods to the coast. For example, Freund and Rocha (2010) highlight the importance of getting goods through transit countries in Africa while Behar, Manners and Nelson (2009) find that your neighbor‟s logistics quality positively influences your own exports. Regional policy co-ordination on reducing obstacles to trade is particularly important for SADC countries, many of which are landlocked.

20
gender in Colombia are less than five percentage points. Thus, these may not make much difference to the final estimates in practice.

# **IFLS panel household survey data: Indonesia**

The IFLS includes a total of 54,000 household observations over five waves from 2,556 subdistricts in 26 provinces. Focusing on the socioeconomic and health aspects of the households, the survey was conducted for the first time in 1993 covering 13 of the total 26 provinces in the country. In a sample of 22,000 individuals from 7,224 households, the survey collected data on individual respondents and their families (households) in addition to data on communities, health, and education facilities. In 1997/98, the second wave was administered to the same respondents with a recontact rate of 94.4 percent. That survey had the particularity to account for the economic and political crisis in Indonesia. The third wave in 2000 was managed to recontact 95.3 percent of the first wave sample while the fourth and the fifth rounds conducted in 2007/08 and 2014/15 recontacted 93.6 and 90.5 percent, respectively, of the first wave sample (Strauss et al. 2016, cited in Setiawan, Tiwari, and Rizal 2018).

We measured poverty based on per capita consumption expenditures with the national poverty line following Setiawan, Tiwari, and Rizal (2018). The IFLS accounts for the expenditure of the consumption of 37 food items over a seven-day recall period and various nonfood items.<sup>4</sup> The nominal consumption aggregate is both temporally and spatially deflated.<sup>5</sup> In addition to poverty, we identified vulnerable people with a vulnerability line, which is set at 1.5 times the poverty line.

Roberts, Sander, and Tiwari (2019) highlight the importance of classifying urban areas based on their functionality instead of mere population size in Indonesia. Following Duranton (2015), we defined the following four location categories:
is most likely (Ceccato et al., 2014). A range of climatic parameters can be obtained from satellite imagery, and although the accuracy of satellite data is not always at the level of measurement stations, they provide almost real-time data on rainfall, temperature, evaporation, vegetation and land cover that are especially efficient in remote areas where other measurement infrastructure is lacking.</mark> <mark>In addition to the monitoring systems that are based on collecting environmental sensor data, such as temperature and rainfall, we may, in the future, also see warning systems that are based</mark>

> 3 For example, a publicly funded pilot project in Turkey provides locally relevant information to farmers in Kastamonu province, where producers maintain orchards susceptible to frost and pests (Donovan 2011). Initially, nationally aggregated weather data collected in urban areas was used but proved to be inaccurate and of limited use to farmers in the provinces, because of differing microclimates from farm to farm and differences in temperature, humidity, precipitation, and soil fertility. Five additional meteorological stations and 14 reference farms were then established to collect data on these variables, enabling accurate pest monitoring.

16
, Mining Manufacturing Mfg - low tech Mfg - medium Mfg - high- Business Other services<br>fishing tech med. tech services<br>Backward GVC Forward GVC<br><!-- End of picture text --> Source: Own computations. Data: OECD TiE and TiVA data, covering 48 countries (see Appendix 2) and 45 sectors for the period 1995-2018. Note: GVC participation-employment (GVC participation-income, resp.) elasticities at the country-sector level have been estimated using an instrumental variables regression approach with employment (in natural logs) as dependent variable and the instrumented trade variables (in levels and natural logs) as independent variable. The panel data regressions control for country-sector, country-time, and sector-time fixed effects. Only results significant at the 10% level and above the critical value for the Kleibergen-Paap test statistic are reported. For more information, see appendix 1.

A closer look at broad sectors suggests that the employment and income benefits from GVC participation are driven by manufacturing. Manufacturing job growth linked to export growth is higher when those exports are re-exported by a country’s trade partners (i.e., forward GVC participation) as opposed to job growth linked to growth of imported inputs in exports (i.e., backward GVC participation), as evidenced by the higher trade-employment elasticities for the former type of trade engagement (0.57 percent vs. 0.32 percent in Figure 5, top panel). The difference is most striking in the high-tech manufacturing sector which includes electronics, machinery, transport equipment and chemicals. In 9
geographical and weather related indicators were originally at the district level. These district indicators were aggregated to sub-region means using sampling weights provided in the Nepal DHS.

The first stage results reported in Table 1 indicate a strong correlation between conflictinduced casualties and the proportion of a sub-region that is forested in 1994. This conclusion holds for when state-caused casualties and Maoist-caused casualties are measured separately and when they are combined. In regressions that include all six geographical and weather indicators, the coefficient on 1994 forest coverage is statistically significant. Forest cover remains significant when it is used as the only instrument in linear or binary form. The empirical results in Table 1 confirm the theoretical intuition provided above for the correlation between 1994 forest cover and the number of conflict-induced casualties, thus validating our choice of this variable as an instrument.<sup>3</sup> To be able to implement a difference-in-difference methodology similar to that in Angrist and Kugler (2008), we converted forest coverage into a 0-1 indicator where geographical subregions with forest-coverage exceeding the 75<sup>th</sup> percentile value were classified as “moreforested”, and sub-regions with forest-coverage below the 75<sup>th</sup> percentile were classified as “lessforested.” Since Nepal in general is quite heavily forested, a higher than average benchmark (such as the 75<sup>th</sup> percentile value) was required to indicate regions that have relatively more cover. Note that the first stage results are robust to transforming the dependent variable (total conflict-induced casualties) from levels into growth rates over time. Furthermore, our use of a categorical functional form for the first stage (as opposed to a linear functional form) is reaffirmed by a test that plots growth rates in casualties against a linear measure of forest cover in 1994. This test indicated that the relationship between growth rates and 1994 forest cover is not 10
: Republic of Serbia, Republican Statistical Office, Labor Force Survey 2002 (October), Bank staff calculations For example, in Poland youth unemployment rate is above 40 percent. In Bulgaria and Slovakia it is between 35 and 37 percent. On average, the youth unemployment rate in CEE is close to 25 percent. This is almost twice as high as the average youth unemployment rate for the OECD countries. Unemployment rates of such magnitude are likely lead to social tension.

The disproportionately high youth unemployment rates largely reflect higher levels of labor turnover among young workers, who tend to change jobs more often than older workers, presumably looking for the best worker-job match. However, they may also be associated with labor market rigidities and barriers created by stringent employment protection legislation, which favors insiders (workers with secure jobs) at the cost of outsiders (the unemployed, contingent workers). For example, an extremely high fraction of young labor market entrants among the unemployed in FYR Macedonia can be at least partly attributed to high degree of job protection granted to incumbent workers (World Bank, 1998). Another possible source of high youth unemployment is the minimum wage set at above market clearing level. For example, in economically depressed, high unemployment regions of Poland the minimum wage accounted 27
739*|-0.777*|
||(0.142)|(0.138)|(0.137)|(0.413)|(0.413)|(0.412)|
|Observations|1,980|1,980|1,980|1,980|1,980|1,980|
|Demographic and employment controls (year 2000)|NO|YES|YES|NO|YES|YES|
|Median income control(year 2000)|NO|NO|YES|NO|NO|YES|

_Note:_ Robust standard errors in parentheses. *** p < .01 ** p < .05 * p < .1. The dependent variable is the change from 2000 to 2015 in the informality rate, measured as the percentage of employees without pension or retirement benefits at work. The explanatory variable is the change in the log of exports-to-workers and imports-to-workers from 2004 to 2014. Demographic and employment controls include the proportion of rural population, the proportion of population aged 15 and more who are literate, total population, and the share of employment in the primary, secondary and tertiary sectors of economic activity (all variables in the year 2000).

# 4. Trade and local labor market effects: Extensions

## 4.1 Geography: North versus South

Geographical distance is a major determinant of GVC participation and Mexico’s states and municipalities in the North are more closely integrated in GVCs (World Bank 2020, and section 4.4). Geographical proximity to the United States could thus play a major role in determining the local labor market effects of trade. Rather than relying on distance measured in kilometers which does not account for trade barriers, we rely on a measure of travel time to one of 44 entry ports to the United States following Blankespoor at al. (2017). We then split municipalities into the North and South, based on a state’s geographical distance
> 1 for _n_ ≠ _i_ and _dnn_ = 1. Land and labor are the two factors of production. Workers are mobile across locations within country but not across countries. They have idiosyncratic draws for preferences for each location within their own country.

**Consumer preferences** Preferences for a worker in location _n_ depend on goods consumption ( _Cn_ ), residential land use ( _Hn_ ) and an idiosyncratic amenity shock to the utility from residing in that location:

The goods consumption index ( _Cn_ ) is defined over consumption of a fixed continuum of goods _j_ ∈ [0,1]: The price index for goods consumption ( _Pn_ ) is: The idiosyncratic amenity shocks capture the idea that workers have heterogeneous prefer6