rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10221

# **Abstract**

This paper analyzes the risks to child development and school readiness among children under age 6 in Pakistan. Drawing on a nationally representative telephone survey conducted between December 2021 and February 2022, it presents the first nationally representative estimates of child development for children under 3 years of age and school readiness for children 3 to 6 years of age, using internationally validated instruments. The paper examines how risk factors such as parental distress, lack of psychosocial stimulation, food insecurity, low maternal education, no enrollment in early childhood education, and living in a rural area are associated with children’s outcomes. The data indicate that more than half (57 percent) of parents with children under age 3 were distressed and that 61 percent of households reported cutting down on the size of or skipping meals since the start of the pandemic. The data reveal that over half of parents fail to engage in adequate psychosocial stimulation with their child and enrollment in early childhood education is very low (39 percent). The paper finds that child development outcomes decline rapidly as the number of risks increase. Specifically, for children under 3 years, lack of psychosocial stimulation at home and higher levels of parental distress were most significantly associated with lower child development levels. For a child aged 3 to 6 years, early childhood education enrollment and the amount of psychosocial stimulation the child receives at home had the strongest association with school readiness scores.

This paper is a product of the Education 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 ahasan1@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
>5</sup> but not in Chile (Modrego et al., 2016), and Peru (Escobal and Ponce, 2016). However, the results from these country studies are not directly comparable due to differences in the definitions used in each country for poverty lines, income, and other indicators, and differences in the level of aggregation of territorial units. In addition, none of these studies uses regional price indexes to adjust incomes and poverty lines for cost-of-living differences.

Related to this literature are studies focusing on the factors shaping territorial income differences and their evoluation. Among these factors are (i) physical geography and natural endowments (Olfert et al., 2014); (ii) human capital and the tendency of people to sort spatially in response to labor market, amenities, cost of living, and cultural considerations; (iii) place-based investments in physical and technological infrastructure and institutions of importance to economic activity and social life (e.g. financial services); and (iv) the spatial structure of economic activity, which is shaped by agglomeration forces, market access, learning externalities, and migration frictions, among other factors.

In Latin America, territorial differences in labor income have been explained by differences in fundamentals, including resource endowments (Mesquita Moreira et al., 2013), human capital (Skoufias and Lopez-Acevedo, 2009; Acemoglu and Dell, 2010; Modrego and Berdegué, 2016),<sup>6</sup> local

> 4 These countries include Brazil, Chile, Colombia, Ecuador, El Salvador, Guatemala, Mexico, Nicaragua, and Peru. 5 Davalos et al. (2015) reach a similar conclusion of income convergence using municipality-level income data for Mexico from 1990 to 2010, while Lopez-Calva et al. (2021) find absolute convergence from 1992 to 2014 using a unique five wave panel dataset at the municipal level. The convergence process in both cases stemmed from a combination of positive developments in poor municipalities and stagnation or negative growth in richer
Note that, in a repeated cross‐section setting, y�,�� is unobserved because individuals are not followed across years. Thus, the unexplained part of changes in earnings will only be possible to estimate for aggregate, anonymous quantiles of the distribution.

# **3. DATA AND DESCRIPTIVE STATISTICS**

# **3.1 DATA SOURCES**

Most of the empirical work done on labor markets in Europe uses the harmonized EU‐LFS quarterly labor force survey. This survey represents an invaluable data source for labor economists. However, public access microdata do not include information on earnings of workers. We thus use household surveys harmonized by the Luxembourg Income Study (LIS) center. These surveys include information on both employment characteristics and earnings of individuals, allowing to carry out the decomposition analysis detailed before. LIS harmonizes different household surveys to a common standard to assure comparability. In this work we use the German Socio‐Economic Panel editions of 1994 and 2013, the Household Budget Survey of 1992 and the EU‐SILC (Statistics and Income Living Conditions) edition of 2013 for Poland, and the Household Budget Survey of 1990 and the EU‐SILC edition of 2013 for Spain<sup>6</sup> .

The main variables of interest of our analysis are occupations and labor related earnings. With respect to occupations, we classify them into three categories based on their most intensive task, using O*NET task content information: 1) routine task intensive jobs; 2) non‐routine, manual task intensive jobs; 3) non‐ routine, cognitive task intensive jobs. In Appendix 2 we provide a detailed description of how we construct this classification. With respect to labor related earnings, we use annual earnings coming from wage employment. Due to the limitations that household surveys usually have in correctly capturing self‐ employed income, we exclude self‐employed from our analysis. Self‐employed represent between 9% and 15% of the total employment in the countries included in our
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Figure 2.3. Time required to import goods from Douala to Ndjamena via road and rail**

<!-- Start of picture text --> a. Total time (hours) b. By step (% of total time)<br>1000 100%<br>800 80%<br>600 60%<br>400 40%<br>200 20%<br>0 0%<br>Rail/road Road Rail/road Road<br>Port Transport<br>Port Transport Locomotive interchange Administrative<br>Locomotive interchange Administrative<br>Border Border<br>Source: Data collected from World Bank 2008; World Bank 2007; AICD ports and railway databases; and the Camrail Web site<br>(http://www.camrail.net/en/prestation_fret.html).<br>Figure 2.4. Cost of importing goods from Douala to Ndjamena via road and rail<br>a. Total cost (US$ per tonne) b. Composition of cost (% of total cost)<br>300 100%<br>200 75%<br>100<br>50%<br>0<br>25%<br>Road/Rail Road<br>0%<br>Port Travel Road/Rail Road<br>Locomotive interchange Administrative<br>Border Port Travel Locomotive interchange Administrative Border<br>modes<br>goods via alternative transport<br>Costs (US$ per tonne) of importing<br>alternative transport modes<br>Share of costs of importing goods via<br>alternative trasnport modes alternative transport modes<br>Time (hours) to import goods via Share of time to import goods via<br><!-- End of picture text --> _Source:_ Data collected from World Bank 2008; World Bank 2007; AICD ports
concerns the measurement of household poverty. All LSMS and SES surveys include household per capita consumption expenditures (PCE), the variable typically used in poverty analysis. DHS and MICS2 data, however, do not include those variables. In this study, quintiles based on per capita consumption expenditures are used when available. In other datasets, an index of household consumer assets and housing characteristics (an economic status index) was used to classify households into quintiles (following Filmer and Pritchett 2000). The exception is the SES from Cambodia 1999 in which there was a problem in the collection of expenditures data. An economic status index is therefore used in that survey to classify economic status.<sup>7</sup>

# **3. Prevalence of disability and its association with household economic status**

The first issue these data can be used to explore is the prevalence of disability and the association with household economic status. Prevalence estimates range between 0.13 (Myanmar) and 2.77 (Jamaica 2000) percent of the population as having a disability (Table 4). These numbers are consistent with those compiled by the United Nations statistical database on disability (DISTAT).<sup>8</sup> In that source of over 65 surveys and censuses between 1970 and 1992 in developing countries, the mean prevalence rate for the entire population

> 6 Results for Jamaica 2000, Myanmar 2000, and Sierra Leone 2000 are available from the author on request. 7 Consistent with typical poverty analysis, quintiles are derived on the basis of the distribution of people across the socio-economic status measure. Specifically, quintiles are defined such that 20 percent of youth live in each quintile.

> 8 Available online at <u>http://unstats.un.org/unsd/demographic/sconcerns/disability/disab2.asp.</u>

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.2 Sample**

After cleaning and merging the trade flows data with information on market access, we are left with a dataset covering 186 countries that report trade flows directed to the United States and/or the EU. For consistency, we define the EU as the 15 countries which were part of the union from the beginning to the end of the sample period (1995-2016), namely Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, United Kingdom. We exclude trade flows from the United States or the EU in the other imports’ dataset, as well as imports coming from a few small islands or territories characterized by conflicts.

The list of products considered in the analysis is as follows. We started as exhaustively as possible, including all available products at the 6-digit HS level (total number of products imported is 5,018 for the EU and 5,031 for the United States). We then excluded oil and arts objects, because oil dominates exports of several countries and the supply response for both types of products is likely to be different from other products. Next, we also exclude products which report missing values on their MFN tariffs in any year and products for which the MFN

> 18 The specific name in WITS are: European Union GSP, European Union GSP for LDCs (or EBA) and EU Special Incentive Arrangement  (or GSP+).

> 19 More specifically, for years 1998 to 2005, we used the data from Frazer and Van Biesebroeck (2010), which they retrieved from the US Trade Representative and US International Trade Administration. Data for years 1995-1997 and 2006-2016 have been recovered from multiple sources, but primarily from the AGOA website.

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but reliable indicators of the market characteristics that might affect agent transactions based on the consulting firm’s long experience in Kinshasa.

We include a dummy variable for high income and another for low commercial density in the regressions that follow. To the extent that agents are an effective way to reach low income market segments that are typically financially excluded, we would expect a negative relationship between the high income dummy and agent transactions.<sup>24</sup> The level of commercial development is an indicator of both demand for financial transactions and the ease with which many clients can perform them, especially during the course of the business day. We therefore expect the low commercial density dummy variable to be negatively linked to the number and volume of agent transactions. Note that the market characteristics that we include in the regressions are timeinvariant because it was not possible to create reliable measures that varied over time. At the same time, however, we argue that variables such as these are slow to change and thus they should do a good job of accurately sorting municipalities into a few bins within the tight window for which we have agent transactions data.

Finally, and as described above, Experts SARL created variables to describe the level of financial development in each municipality, including an overall assessment of financial density as being high, medium, or low, and an assessment of the predominant type of financial institution (banks, microfinance institutions, providers of mobile banking services). To the extent that the services provided by FINCA agents are effective in reaching market segments that are underserved by other providers, we would expect agent transactions to be higher in locations where there are fewer alternative service providers. Thus, we would expect a negative relationship between the dummy variable for high financial density and our agent transactions variables.

> 24 Our assumption is that higher income clients are more likely to access financial services from providers other than FINCA DRC agents.

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and the synergy effect has a negative and significant coefficient, implying that firms belonging to industries with high asset tangibility (essentially capitalintensive industries) are _less_ sensitive to the synergy effect.<sup>17</sup> Given that these interactions have significant explanatory power, we combine them to address a potential omitted variable bias, and to compare there coefficients in a joint multivariate regression. In columns (4) and (5) we combine one regressors capturing each of the three hypothesis that these interactions try to capture: information, financial constraints or capabilities. We find that typically the same signs, magnitudes and statistical significance levels persist as the ones just discussed, and the interpretations from above are valid when we control for all these effects simultaneously.

# **5 Concluding remarks**

In spite of their growing interest for the profession, firm-level datasets are still rare for low-income countries, and virtually inexistent for African countries. Our exploration of African customs data on firm-level exports revealed a set of stylized facts that are consistent with evidence from previous studies analyzing rich or middle-income countries. We showed that exporters in our set of African countries experiment a lot on export markets, at a low scale and with low survival rates, particularly in the first year. That is, they operate in a difficult environment characterized by very high “infant-mortality rates”. Therefore we investigate more in detail what determines if they survive beyond their first year.

The most striking finding coming out of our analysis —and which could not be observed on the product-level data used by previous studies of export survival—is that exporters of similar goods to the same destination exert a _positive_ externality on new entrants. That is, the more they are, the higher the survival probability of new entrants—although the effect is relatively small. This finding is at first sight surprising, as one might expect that exporters of a given product to the same destination may crowd out each other, either through price competition or simply by offering more choice to buy17 Similarly as
health, employment and social security, housing, and networks and social cohesion.

The FEI takes into account only one measure – the net present value index (IVAN) – derived from project CBAs. The IVAN is equivalent to a ratio of the net present value of future project costs and benefits to the project’s initial investment cost.

The formulas for SEI and FEI are as follows: SEI�w� �Ben_inv�w� �Jobs_inv�w� �Savings_ben�w� �Pov_c/multi �standardised� FEI� IVAN �standardized� where w�, ..., w� are the weights associated with each SEI criterion. Input project data are detailed in Annex 5.

## Transport Project Indicator Weighting

The criteria weighting of SEI variables was done using PCA, with the two specifications for measuring poverty. Table 9 gives the weights associated with each variable using each approach.

Table 9. Criteria Weighting, Transport SEI

|Simple poverty rate|Ben_inv<br>|Jobs_inv<br>|Savings_inv<br>|Pov_c<br>|
|---|---|---|---|---|
||0.614|0.224|0.724|0.224|
|Multi‐dimensional|Ben_inv|Jobs_inv|Savings_inv|Pov_multi|
|poverty index|0.442|0.224|0.839|0.224|

Note: The weights have been determined by PCA with the restriction that all weights must be positive and have a minimum contribution of 10%

> 16 In economic ($) terms, this would likely be considered in the FEI; however, here, our interest is to reduce consumption for environmental reasons. Therefore, the savings is measured in liters and included in the SEI.

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empirical model of the determinants of the number of imported products from a particular trading partner that a policy-imposing economy subjects to new temporary trade barrier investigations. The model relates the number of products under an antidumping, global safeguard, China safeguard, or countervailing duty investigation in a given quarter to the first lag

> 7 Crowley (2011) examines US antidumping data for industries over 1980-2001 and finds evidence in support of this theory at the relatively disaggregated level.

> 8 While Knetter and Prusa (2003) acknowledge the ambiguous theoretical predictions of their model, they find strong empirical evidence at the annual frequency for 1980-1998 linking exchange rate appreciations to aggregated counts of antidumping filings for a sample that includes data from the US, EU, Australia and Canada.

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that female entrepreneurs, in particular lower educated ones and those running smaller businesses, are under-represented among KUR borrowers. If female entrepreneurs had similar access to KUR as male entrepreneurs, one would expect to see similar trends among KUR borrowers as in the population of Indonesian microentrepreneurs. However, female entrepreneurs are less prevalent among KUR borrowers than in the population of Indonesian microentrepreneurs (Figure 11). Whereas on average female entrepreneurs in Indonesia have lower education levels and smaller businesses than male entrepreneurs (World Bank 2022), female KUR debtors have higher education levels and seek larger loans than their male counterparts (Figure 12).

Figure 11: Female entrepreneurs are under-represented among KUR borrowers **Share of KUR borrowers and microentrepreneurs in Indonesia who are female** <!-- Start of picture text --> 49.4%<br>40% 36.7%<br>20%<br>0%<br>KUR borrowers Microentrepreneurs<br><!-- End of picture text --> Source: KUR borrowers: SIKP database; Microenterprises: World Bank 2022 Figure 12: Female KUR borrowers have higher education levels and larger loans than male KUR borrowers **Characteristics of male and female KUR borrowers** <!-- Start of picture text --> 11.7<br>Small KUR<br>8.3<br>88.3<br>Micro KUR<br>91.7<br>5.6<br>Diploma/Bachelor<br>3.7<br>30.1<br>Senior High<br>26.7<br>14.3<br>Junior High<br>14.2<br>46.3<br>Elementary<br>51.5<br>18.3<br>Not married<br>12.4<br>81.7<br>Married<br>87.6<br>0 10
6 percent and most of this increase is due to households shifting their savings to formal sources (45.3 percent higher savings in formal sources). But overall, there is only a 5.2 percent increase in share of formal savings to informal savings. We hypothesize that this could be because the share of formal savings is already 83 percent in the whole sample, as the national government has aggressively promoted financial inclusion. Lastly, we find that 6.5 percent more women have a bank account in the treatment group.

Next, we analyze the extent and nature of household borrowing. Consistent with the program focus, the reach of financial services has been expanded in treatment areas, which are completely un-penetrated by other institutional providers. For instance, less than 1 percent of households have a current loan from micro-finance companies and less than 2 percent have loans from commercial banks in the full sample (directly as individuals). But, 35 percent of the households, have a current loan through SHGs in treatment areas and 12 percent in the whole sample. Overall, 24 percent more households have a current outstanding loan in treatment areas when compared to treatment areas. Anecdotal evidence suggests that most of these borrowers are accessing credit for the first time from institutional sources such as these.

As NRLP has been able to facilitate lending of over $1.2 billion since 2011 (World Bank 2018), we examine if this often reported overall borrowing figure has translated into

> 19 As we do not have data on number of hours worked, we cannot estimate if this transition from casual wage to self-employment has resulted in more productivity (such as higher per hour wage etc.).

> 20 Savings as defined as amount saved and invested in commercial banks, co-operative banks, self-help groups (SHGs), microfinance institutions (MFIs), relatives, friends, post office, landlord, money lenders, chit funds, life insurance, and money saved at home.

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<br>sector, using capital and labor.|We calculate an annual log growth rate of the<br>historical estimates of A and take an average from<br>2000‐2012. We set a minimum value of 0.|
|Growth of the<br>Labor force<br>(growth of LF+<br>LI+ LR)|Growth of the total labor force (i.e.<br>in the modern and rudimentary<br>economy) by country.|We use estimates and projections of the size of the<br>labor force by country from the ILO (available until<br>2030) to find an annual growth rate. For the initial<br>year, we use an average of estimates of the size of|

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<!-- Start of picture text --> crop months to fix the signals at 0 in the data. Z-scores considering a crop calendar resulted generally in<br>outperformed crop-growing only months. The predefined general thresholds recommend for the SPI to<br>Conflict affects general security and the movement of people, crucial for agricultural and fisheries<br>children lived in conflict-affected areas (FAO, IFAD, UNICEF, WHO, and WFP, 2017). Studies indicate that<br>conflict's impact on food security and nutrition worsens with prolonged conflict and weak institutional<br>ACLED data, tracking monthly conflict incidents and fatalities at the district level, was utilized. Conflict<br>violence, or violence against civilians. The analysis considered conflict at both the district and<br>surrounding district levels to capture exposure to nearby conflict, defined as occurring within either one<br>Methods tested for both direct and neighboring district conflict data included percentage deviations<br>from MA and EMA, and the RSI of these EMAs. For neighboring conflict, the averages over the<br>neighboring districts were constructed first, before calculating the moving averages over time. The<br>local/regional conflict is surging or easing. Of the indicators tested, neighboring conflict within one level<br>neighboring conflict fatalities using RSI over a 14-month period based on the EMAofthe last 12 months.<br>Displacement amplifies demand for food and services in host areas and often signifies severe livelihood<br>disruptions. Research indicates significant impacts of forced migration on wages, household income,<br>both were examined, using the same analytical methods as for conflict modeling.<br>an EMA identified as the optimal method. The analysis employed a 6-month time window for the EMA<br><!-- End of picture text -->