rafmacalaba/datause-encoder-data huggingface dataset:
this small 10-day discrepancy to be acceptable and unlikely to drive results. The case is less clear for HBP, which had been rolled out before baseline.<sup>25</sup> Since HBP is mainly a demand-side intervention with limited expected short-term effects on cognitive performances, early participation of parents in the HBP is unlikely to have undermined to our experimental design.

A potentially greater cause for concern is the fact that SPS are reported, at baseline and later on, to be less available in the treatment groups compared to the control group (Table 5). While the point estimates are insignificant for T1 and T3, and again insignificant when the treatment groups are considered jointly (- 7 pp), the T2 villages are significantly less likely to have access to an SPS (-15 pp). Since SPS are expected to be of much better quality than CPS (as we discuss in section 3.2.3 below), this small imbalance, which we attribute to chance, is likely to provide a baseline (relative) cognitive advantage to the control group, therefore driving down our estimates of CPS impacts on cognitive development. This downward bias is likely to be stronger in T2. We return to this then discussing results.

Finally, with regards to attrition, we find no correlation between the random assignment and the probability of not responding both at midline and endline (Column (1) in Table B1). Attrition is not significantly related to a set of variables strongly associated with child development (Column 2). Furthermore, there is also no evidence of differential attrition with respect to baseline controls except for T2 at the endline follow-up, where children with a high height-for-age z-score were more likely to attrit from the sample. The addition of households to the sample at midline causes differential attrition to become significant for T1 at midline since fewer households were added to this group (Column 3). The overall level of child attrition is around 10% at both midline and endline, and mostly due to seasonal migration.

**3.2.3 Quality of CPS and other preschools**

We construct measures of preschool quality based on data from two sources, classroom observations and parent reports.

# **_Preschool Quality Measures_**

We use the teacher survey and classroom observation tools to assess how new CPS compare to SPS and IPS in terms of structural and process quality. We compare the different preschools in three dimensions of structural quality (teacher characteristics, equipment, classroom setting) and two dimensions of process quality (curriculum and pedagogy, teacher-child interactions).

Individuals quality measures are summarized using aggregate measures for each of the five dimensions of structural and process quality, calculated as the first principal component of individual variables (Table 4, panel “Preschool comparison”).

> 25This is probably due to the fact that setting up an HBP takes much less time than building a CPS.

15
term or long-term is far from a settled point in studies from low- and middle-income countries (See Akyeampong et al., 2023).

There is growing evidence of the benefits of quality pre-primary education as a costeffective catalyst for improvements in learning outcomes and long-term economic benefits later in life. Compared to other educational policies to improve foundational learning, global experts propose that investing in quality pre-primary education can help address learning inequities and reduce deficits by the time children enter primary school (Global Education Evidence Advisory Panel, 2020). This is confirmed by results from international large-scale assessment studies, which have shown that enrollment in pre-primary education results in higher average learning outcomes in reading, mathematics and science (Mullis et al., 2020; Mullis et al., 2023). Yet, even when investing in improving access to and quality of early childhood education can be seen as smart policy, most of the studies that have investigated this linkage come from high-income countries (Holla et al., 2021). Fortunately, more and more evidence on this linkage is being generated in low- and middle-income contexts to support the investment in quality early childhood education (Augsburg et al., 2022; Hasan et al., 2019; Nakajima et al., 2021).

Hence, this study provides a window into one of the less explored and more important policy aspects of young children’s education: what goes on in the early childhood education classroom in a lower-middle-income setting such as Pakistan – specifically in South Punjab – and how early childhood settings are associated with children’s cognitive and socioemotional development. It attempts to fill a knowledge gap by describing what relevant skills pre-primary aged children can do and the quality of classrooms in which they study. We also report on how children’s skills and classroom quality vary by student, teacher, and school characteristics and assess the correlations between these variables to understand the role that quality may play in supporting young children’s acquisition of school readiness skills. We use a large, representative dataset of public schools in South Punjab to answer the following core research questions:

- 1: What do pre-primary classrooms in South Punjab look like?

- 2: What is the structural and process quality of these classrooms?

- 2.1: How does quality differ by classroom and teacher factors?

- 3: What can children in pre-primary classrooms do?

- 4: How are children’s early learning and development related to individual, teacher, and class factors?

# **Data**

The data presented in this paper draw on the Baseline Survey on Early Childhood Care and Education (ECCE) carried out under the Education Component of the Punjab Human Capital Investment Project (PHCIP). Child-level data on the Anchor Items for the Measurement of Early Childhood Development: Direct Assessment (AIM-ECD DA) was collected from January to April 2022. AIM-ECD DA data was collected by 32 certified enumerators. Observations of classroom 3
# **7. References**

<mark>Alder, Simon, (2014). "Chinese roads in India: The effect of transport infrastructure on economic development." Working Paper, Univ. North Carolina, Chapel Hill</mark> Allen, Treb and Costas Arkolakis, (2014) “Trade and the Topography of the Spatial Economy," The Quarterly Journal of Economics, vol. 129(3), pages 1085‐1140. 1.

Allen, Treb and Costas Arkolakis, (2019) “The Welfare Effects of Transportation Infrastructure Improvements" Working Paper.

Anderson, James E and Eric van Wincoop, (2004) “Trade Costs,” Journal of Economic Literature, 42 (3), 691–751.

Caliendo, L. and F. Parro, (2015) “Estimates of the Trade and Welfare Effects of NAFTA,” The Review of Economic Studies, 82 (1), 1–44.

Center for International Earth Science Information Network ‐ CIESIN ‐ Columbia University. 2016. ‘Gridded Population of the World, Version 4 (GPWv4): Population Count Adjusted to Match 2015 Revision of UN WPP Country Totals.’ Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4SF2T42.

de Soyres, F., A. Mulabdic, S. Murray, N. Rocha, and M. Ruta, (2018) “How Much Will the Belt and Road Initiative Reduce Shipment Times and Trade Costs?,” Policy Research working paper; no. WPS 8614. Washington, D.C.: World Bank Group de Soyres, F., A. Mulabdic, S. Murray, N. Rocha, and M. Ruta, “The Belt and Road Initiative: A Structural Analysis”, forthcoming Defourny, P. (2017) ‘ESA Land Cover Climate Change Initiative (Land_Cover_cci): Land Cover Maps, v2.0.7.’ Centre for Environmental Data Analysis.

Djankov, Simeon, Caroline Freund, and Cong S Pham, (2010) “Trading on Time,” Review of Economics and Statistics, 92 (1), 166–173.

Donaldson, Dave, (2018) “Railroads of the Raj: Estimating the Impact of Transportation Infrastructure,” American Economic Review, 108 (4‐5), 899–934.

Duranton, G., P. M. Morrow, and M. A. Turner, (2014) “Roads and Trade: Evidence from the US,” The Review of Economic Studies, 81 (2), 681–724.

Fajgelbaum, Pablo and Stephen Redding, (2014) “External Integration, Structural Transformation and Economic Development: Evidence from Argentina 1870‐1914,” NBER Working Paper 20217.

Fujita, M., P. Krugman and A.J. Venables (1999) ‘The spatial economy; cities, regions and international trade’, MIT press, Cambridge MA.

Gaulier, G. and Zignago, S. (2010), ‘BACI: International Trade Database at the Product‐Level. The 1994‐ 2007 Version’, CEPII Working Paper, N°2010‐23 Glaeser, E. and W. Xiong. (2018), ‘Urban productivity in the developing world’, Oxford Review of Economic Policy, 33.3, 373‐404.

Hummels, David L and Georg Schaur, (2013) “Time as a Trade Barrier,” American Economic Review, 103 (7), 2935–2959.

Lall, Somik V. and Lebrand, Mathilde, (2019) “Who wins, who loses? Understanding the Spatially Differentiated Effects of Belt and Road within Central Asia”.

Melo, Patricia C., Daniel J. Graham, and Robert B. Noland. (2009) "A meta‐analysis of estimates of urban agglomeration economies." Regional science and urban Economics 39.3: 332‐342 30
# **I. Introduction**

An efficient logistics sector is an integral part of a sustainable growth strategy for any country. Reducing costs and improving the quality of logistics services allows for more efficient and timely movement of goods which integrates domestic markets and improves access to foreign markets. Better logistics performance is shown to be strongly associated with trade growth, export diversification, and economic growth (Arvis et al., 2010; Behar et al., 2013). The logistics of any supply chain - be it domestic, regional, or global - encompasses freight transportation, warehousing, border clearance, payment systems, and other functions outsourced by producers and traders to specialized providers.

Freight forwarders are some of the key specialized providers in the logistics network. A freight forwarder is an individual or firm that organizes shipments for other individuals or firms, in order to get the goods from the producer to a market (whether to a direct customer or to a distribution point). The freight forwarder need not move the goods itself, but arranges shipping by contracting with a carrier to move the goods (by ship, airplane, truck, railroad, or a combination of these) and arranges storage. Freight forwarders that handle international shipments also assist with preparation and processing of customs and other documentation needed to cross international borders and deal with the relevant government agencies. Freight forwarders’ familiarity with the particularities of logistics and administrative hindrances in a given setting means that their views offer important contextual information for understanding international trade frictions.

Both the highly-influential World Bank’s Doing Business indicators on the costs of trading across borders as well as the World Bank’s Logistics Performance Indicators are to a large extent based on data collected from a small number of freight forwarders in each country. Yet, while assessments of the general trading environment and the ease of trading across borders often rely on such indicators, there is little evidence about freight forwarders’ operations within a country based on comprehensive data. This study fills in this knowledge gap by providing evidence on operational behavior of freight forwarders and how it is affected by delays in border procedures based on a survey of 153 freight forwarders in Serbia conducted in 2014. This survey was conducted as a complement to a project estimating the value of time and uncertainty in the context of trade facilitation reforms in the Western Balkans in conjunction with the South East Europe Trade Logistics Project. An important purpose of the survey is to understand trade-offs 2
conclusions of this paper study and relate them to other works.2

# II. A Modgl fo a Dual Exchange Rate System

There has been considerable interest in the functioning of dual exchange rate markets and the effect of their presence on various macroeconomic variables. Two excellent surveys on the existing literature have recently been written by Lizondo (1990) and Agenor (1990a). Analytical models for parallel markets can be classified into the following four categories (Agenor,1990a): smuggl'ng and real tradelmodels, the monetary approach, portfolio and currency substitution models and, most recent, models of dutl markets with leakages.

In this section ye present a portfolio model based on Flood (1978), Lizondo (1987) and Kiguel and Lizoneo (1990), which serves as the basis fo- the cc. nometric estimations presented in the next section. The model assum'e. a small open economy with one traded good and one non-traded good. All commercial transactions take place at the official rate Eo, which crawls at a rate w. All financial transaktions take place at the parallel rate 16, which is determined by the market. Private sector nominal wealth, W, is t

## _PM+ER,F_

where M is nominal money stock and F is the stock of foreign assets held by private agents. Private sector consumption of traded and non-traded Soods respectively is

**_c,-aa_** _(m+fq_

2 In particular we will compare them to the studies for Argentina (Kamin(1991)], Mexico (Raminsky(1991)], Sudan [Elbadawi(1991)) and Tanzania (Kaufman and O'Connell(1990)J. aswell _as_ the comparison of the European HER arrangements with some of the Latin American HERs (Marion(1991)l prepared for the project on the "Macroeconomic Implications of Multiple Exchange Ratc Systems," at the World Bank.

3
bottom 40% in the 124 countries for which we have data was 16.5%, with as standard deviation of 4.2pp. Table 2 shows the evolution of the income share across different shared prosperity scenarios for the 10 year growth scenario. Under , the mean income share of the bottom 40% increases to 23.7% and declines to 11.4% with . mm= 2% mm= −2%

**Table 1: Mean Gini under various shared prosperity scenarios (with 10 year historic growth in the mean)**

||**m=0%**|**m=1%**|**m=2%**|**m=-1%**|**m=-2%**|
|---|---|---|---|---|---|
|**2011**|40.8%|40.8%|40.8%|40.8%|40.8%|
|**2020**|40.8%|39.0%|36.5%|42.4%|43.8%|
|**2030**|40.8%|36.9%|31.2%|44.0%|46.6%|

It should be noted that the changes in inequality implied by our simulations are rather exceptional when compared to what has been experienced in the past. In particular, countries with already low levels of inequality become even less unequal at a very rapid rate. For countries which start with high inequality, the level of inequality simulated for 2030 is not unprecedented, however. In our simulations, the bottom 40% at any point are helped to catch up with the rest of the population, so this would lead to a perfectly equal society if repeated infinitely. Therefore, it is not surprising that we obtain such low levels of inequality.

**Table 2: Mean income share of the bottom 40% for various shared prosperity scenarios (with 10 year historic growth in mean)**

||**m=0%**|**m=1%**|**m=2%**|**m=-1%**|**m=-2%**|
|---|---|---|---|---|---|
|**2011**|16.5%|16.5%|16.5%|16.5%|16.5%|
|**2020**|16.5%|18.1%|20.0%|15.1%|13.8%|
|**2030**|16.5%|19.8%|23.7%|13.7%|11.4%|

Indeed, inequality-reducing transformations of the distribution are by definition bounded to the limit imposed by the equidistribution. The bottom 40%’s income share cannot exceed 40% of income by construction. Hence achieving any shared prosperity premium is more difficult for more egalitarian countries and as countries become more so. This implies that the performance of a country in terms of shared prosperity depends not only on its premium but also on the initial level of inequality and the growth rate in the mean – the higher the latter the easier it may be to obtain a given premium. mm 20
Table 9: Summary Statistics- Prices

|Semi-annual change|Not Constrained|Constrained|Total|Diference|t-statistic|
|---|---|---|---|---|---|
|Output Price|-0.009|-0.019|-0.015|0.010|1.522|
|Raw Material Price|0.068|0.079|0.074|-0.010|-1.644|
|Log Wages|0.046|0.079|0.066|-0.033|-0.810|
|Interest rate*|-0.003|0.001|0.000|-0.004|-7.978|

**_Note:_** Financial constraint indicator is internal finance, variables defined in logs. * Due to low response rate, the interest rate is calculated as the average over sector and time. The null hypothesis that the difference in means between constrained and unconstrained firms is equal to zero cannot be rejected for all variables at the 5% significance level except for interest rates.

Table 10: Summary Statistics - Firms Characteristics

||Not Constrained|Constrained|Total|Diference|t-statistic|
|---|---|---|---|---|---|
|N. Workers|369.93|221.53|271.92|148.41|8.54|
|Age|16.68|15.15|15.67|1.53|1.91|
|Garment|0.24|0.31|0.28|-0.07|-2.47|
|Textile|0.33|0.25|0.27|0.08|2.96|
|Food|0.16|0.14|0.15|0.02|0.98|
|Leather|0.09|0.14|0.12|-0.05|-2.44|
|Electronics|0.04|0.09|0.07|-0.05|-3.36|
|Chemical|0.14|0.08|0.10|0.07|3.68|

**_Note:_** Sector variables are indicator variables. The null hypothesis that the difference in means between constrained and unconstrained firms is equal to zero is rejected for all variables at the 5% significance level except for age.

Table 11: Constraints and Shocks DPROBIT- Robust standard errors Cluster Standard Errors on Firm id Reported Marginal Effects

|Become Constrained|Acc|ess|Inte|rnal Finance|
|---|---|---|---|---|
|shock|0.389|0.387|0.322|0.312|
||(2.17)**|(2.22)**|(2.30)**|(2.27)**|
|Initial R|0.018|0.016|0.013|0.011|
||(2.98)***|(2.52)**|(2.33)**|(2.13)**|
|Initial K|-0.007|-0.006|-0.012|-0.008|
||(1.48)|(1.15)|(2.57)**|(1.91)*|
|time trend|-0.027|-0.057|0.018|0.016|
||(4.35)***|(3.32)***|(1.41)|(3.01)***|
|Control for Firm Characteristics|no|yes|no|yes|
|Observations|1217|1216|1216|1217|
|N.frms|250.00|250.00|250.00|250.00|
|Pseudo R2|0.02|0.03|0.03|0.02|
|Log likelihood|-533.08|-527.38|-348.78|-351.99|

Robust z statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 31
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The audit function in the URA remained weak in spite of the reform, though the Large Taxpayers Department is better positioned than the rest of the agency. The LTD reported a comprehensive audit presence of about 11% in 2001 and also reported using third party information to cross-check taxpayer returns (Table 3 summarizes large taxpayer audit coverage across several countries). The Internal Revenue Department registered an audit presence of about 2.6% in 2001 for filing corporate income tax and VAT taxpayers.

# **TABLE 3. AUDIT COVERAGE OF LARGE TAXPAYERS**

|RevenueAuthority|Audit Coverage|
|---|---|
|KRA(2001)|38%|
|SENIAT(1999)|6%|
|SUNAT(1999)|11%|
|URA(2001)|11%|

_Source: Author’s calculations based on ARA and MOF data._ In sum the cases show improvement in taxpayer registration, especially in Peru, Kenya, and South Africa. The cases show that the more autonomous ARAs have invested their resources wisely in their large taxpayers offices. SUNAT and the KRA have focused on their LTOs and have managed to improve performance considerably as a result. SARS has made progress in expanding the tax rolls and improving filing compliance in the income tax. The KRA has also made some progress on expediting refunds. The cases show declining performance in Venezuela and stagnation at best in Uganda. There seems to be a positive association between the greater autonomy of SUNAT, KRA, and SARS, and their relatively better performance.

With respect to compliance management SUNAT has demonstrated progress in comparison to the pre-reform situation, though its performance has not been exceptional in this regard. The Mexican case suggests that while the SAT’s performance rates positively in comparative perspective, there has not been much improvement as a result of the reform. Though SENIAT made some progress with its large taxpayers office, the impact of the reform was limited. The KRA made significant progress since the reform, instituting new audit guidelines and procedures, introducing field audits, improving audit productivity, and boosting audit coverage in the LTO significantly, yielding a high 38
Another potential problem with the CHNS is that we may underestimate the migration rate because migrants in between surveys cannot be identified, and hence we may underestimate the impact of a household member’s out-migration on women’ behavioral adjustment.

As its name implies, the CHNS provides multiple measurements of health. We take advantage of some of these, and analyze how different facets of a woman’s health ─ body mass index (BMI), self-reported health, and stress as measured by blood pressure and alcohol consumption ─ are affected by household member migration status. The available health measures are not ideal for our purposes and we will not be able to conclusively say whether and how health is affected. The surveys also include questions about which household member has the primary responsibility for the farm, and any fishing and gardening activities. We use this as one of our outcome variables as well to test whether women in migrant households are more likely to report being in charge.

It should be noted that some important variables are less well measured in the CHNS. For example, operated land amounts are often missing. We expect this to be a key covariate but can only control for whether households report having land or not.

# **4. Labor allocation, women’s migration and the left behind**

As a background to our analysis of the impacts on women of living in migrant households, we describe labor force participation rates and allocation across activities by gender and their evolution over time. We also examine women’s migration and its determinants, and women’s non-migration. A key result of the section is that more women are in fact being left behind.

# **_4.1. Gender differences in labor allocation_**

Using the four survey rounds of the CHNS as a pooled cross-section we categorize respondents aged 16-70 into four sectors of activity at each date: “migrant” employment (for which migration is necessary), rural agricultural self-employment, rural off-farm self-employment (family business) and rural local wage employment.<sup>8</sup> Table 1 also presents overall labor force participation rates for men and women in this age group as well as mean age and years of education for those involved in each activity for 1997,

> 8   Labor migration can be to other rural areas although the bulk of employment for which migration is necessary is urban.

9