rafmacalaba/datause-encoder-data huggingface dataset:
public schools, but 96% of private ones. A library is present in 20% of public schools, but 62% of private ones. This suggests why parents may pay for private pre-primary when public is free.

In the 2000 Census, about 32.8% of Brazilians were below the indigence line, defined as the income necessary to consume the FAO minimum daily caloric intake.<sup>13</sup> Based on the most recent household expenditure survey conducted in Brazil,<sup>14</sup> median _per child_ expenditure on private pre-primary education was 74% of the indigence line income. The 5th percentile of _per child_ expenditure was 15% of the indigence line income. This suggests that the indigent (and probably many above this line) could not afford formal, private pre-primary education.

At higher incomes, parents are less likely to send their child to public pre-primary school, less likely to keep their child out of school, and more likely to send their child to private preprimary school than at lower incomes. Figure 3 illustrates this with child-level data from the 2000 Brazilian Census. I examine the school enrollment status of 4-6 year-olds (preschool children), and observe how this varies with per capita household income. From all enrollment and income variables, I net out the effects of many potentially-relevant demographic characteristics.<sup>15</sup> I then plot the residuals and fit a quadratic function. The function is positively-sloped when I consider a dummy for enrollment in private pre-primary, or a dummy for enrollment in any pre-primary. It is negatively-sloped when I consider a dummy for enrollment in public pre-primary. This will be important later, as it supports my assumption that private pre-primary is a normal good.

# **3.2 Municipal governments and their finances**

Brazil has over 5,000 municipal governments, with remarkable financial and political autonomy. They are led by an elected mayor and an elected city council. Legislative, budgetary, and administrative authority are concentrated in the mayor’s office, and the city council is largely responsible for approving the mayor’s policies (Couto and Abrucio 1995; Wampler 2007).<sup>16</sup>

> 13This is 2,288 calories. See Ferreira _et al._ (2003) for computations of three poverty lines for Brazil using 1996 household survey data. The lowest is the indigence line. The authors also present a set of spatial income deflators. 14This is the 1996-97 Living Standards Measurement Survey, carried out by the Brazilian Institute of Geography and Statistics (in Portuguese, Instituto Brasileiro de Geografia e Estat ́ıstica (IBGE)).

> 15I partial out the effects of child age and gender, household size, municipality fixed effects, and several household head variables including gender, migration status, years of education dummies, race dummies, and age dummies.

> 16For a thorough discussion of local politics in Brazil, see Ames (2001) and Kingstone and Power (2008). For a treatment specific to the politics of education, see Plank (1996).

8
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Policy Research Working Paper 7973

# **Abstract**

Longitudinal patterns of child development and socioeconomic status are described for a cohort of children in Madagascar who were surveyed when they were 3–6 and 7–10 years old. Substantial wealth gradients were found across multiple domains: receptive vocabulary, cognition, sustained attention, and working memory. The results are robust to the inclusion of lagged outcomes, maternal endowments, measures of child health, and home stimulation. Wealth gradients are significant at ages 3–4, widen with age, and flatten out by ages 9–10. For vocabulary and sustained attention, the gradient grows steadily between ages three and six; for cognitive composite and memory of phrases, the gradient widens later (ages 7–8) before flattening out. These gaps in cognitive outcomes translate into equally sizeable gaps in learning outcomes. Between 12 and 18 percent of the predicted gap in early outcomes is accounted for by differences in home stimulation, even after controlling for maternal education and endowments.

This paper is a product of the Poverty and Inequality Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at egalasso@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, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
costs categories are on average higher compared to all the other regions. Customs and ports procedures and inland transportation take on average three times more in SubSaharan African countries than in OECD countries. In addition, documentation procedures take four times more in Sub-Saharan countries compared with developed countries.

The rest of the data are from standard sources. The trade data are both from the UN Comtrade database and the IMF Direction of Trade database. GDP and Population are from the World Bank’s World Development Indicators. Gravity variables such as country pair distances, language and colony are taken from the Mayer and Zignago dataset. Country's Capital abundance information is available for 2005 and comes from GTAP 7 database. Simple average Tariffs at 6 digit level are taken from the TRAINS dataset.

# **III. Methodology**

We investigate how three diverse trade costs---completing documentation, inland transit delays, and customs and ports times—affect Sub-Saharan Africa trade volumes. Longer time delays act as a tax on exports, especially on high-value goods, since they are effectively depreciating during the delay. In addition, the exporter must spend capital on the exporting process and storage/transport of the goods during the delay.

We begin by estimating the augmented gravity equation:

> where the _i_ and _j_ subscripts correspond to the exporter and the importer, respectively.<sup>5</sup>

> The dependent variable represents bilateral exports from country _i_ to country _j_ . The variables of interest are the export times for transit, customs and ports, and documents. The coefficient on each represents the effect in percent of trade of a one day increase in that component.  We focus on variables in levels, so that the coefficients are

> 4 The time delays reported in the survey are probably at the lower end of the time it takes to move the average product from factory to ship. This is because the products are chosen so that they do not require cooling or any technical inspections based on use of hazardous materials.

> 5 The times representing terminal port handling and customs and technical control were aggregated due to their very high correlation. (See Table 2)

6
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Tumen, Semih. 2018. “The Impact of Low-Skill Refugees on Youth Education.” IZA Institute of Labor Economics Discussion Paper No. 11869.

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Verme, Paolo. 2016. “The Economics of Forced Displacement: An Introduction.” _Region et Development_ 44: 141-63.

Verwimp, Philip, and Jean-Francois Maystadt. 2015. _Forced Displacement and Refugees in Sub-Saharan Africa. An Economic Inquiry_ . World Bank Policy Research Working Paper 7517.

Wyrwich, Michael. 2018. “Migration restrictions and long-term regional development: evidence from large-scale expulsions of Germans after World War II.” Jena Economic Research papers 2018-002.

24
**<u>Table 1: Correlates with the election of a district nazim</u>**

||Prob. of nazim<br>gettingre-elected|Prob. of nazim<br>gettingre-elected|
|---|---|---|
|Citizen satisfaction with:|||
|Basic health facilities|-0.13<br>(1.88)||
|Schools|-6.70||
||(4.59)||
|Roads|3.29||
||(2.58)||
|Drinking water supply|2.67<br>(2.79)||
|Perceived improvement (over past 12 months) in:|||
|Basic health facilities||-1.01<br>(0.62)|
|Schools||-0.01<br>(0.97)|
|Roads||0.54|
|||(1.08)|
|Drinking water supply||0.702<br>(1.78)|

_Note:_ Logit regression; standard errors in parenthesis _Sources_ : PSLM 2004/05 and data from the Election Commission of Pakistan satisfaction with education, health, roads, and water supply services in a particular district, or whether the service had improved over the past year (as expressed in the PSLM survey of 2004/05), and the re-election of the district nazim.

What does this data on re-election imply? First, incumbency disadvantage reinforces the fact that district nazim elections are indeed competitive and therefore political capture in its crudest form — that is, local monopoly control — is limited. Second, given the indirect election system this result may not be surprising since re-election depends on the support of a few hundred union councilors and may have nothing to do with citizens’ perceptions of service delivery. Third, electoral accountability may exist but voters credit service delivery improvements not to the district nazim but to the provincial government, the prime minister, or other actors, or take other factors into account during elections.<sup>6</sup> To conclude, while proximity of local policy-makers is unequivocally greater than that of provincial or national policy-makers, and local government elections appear to be reasonably competitive, the impact of this greater accountability on service delivery is unclear. The next section delves into incentives for service delivery by examining what district governments actually spend on.

> 6   Electoral manipulation is also a factor that cannot be ruled out.  The provinces with high incumbency disadvantage — Sindh, NWFP, and Balochistan — also had a majority of incumbent district nazims that were in political opposition to the ruling provincial coalition, and anecdotal evidence does suggest that in part their failure to get re-elected was due to the provincial manipulation of the votes of their electoral colleges.

9
|**RO (MW) category**|**Corresponding**<br>**broad industry**<br>**sector/s**|**Number of job**<br>**title categories**|**Years of**<br>**experience**<br>**categories per**<br>**job title**|**Total number of**<br>**wage rates**<br>**specified**|
|---|---|---|---|---|
|Private Secondary School|CSP (Education)|13|Most job titles<br>specified by year; up<br>to 15years|111|
|Public Transport (Buses)|Transport, food,<br>accommodation<br>and ICT (transport<br>and storage)|35|Most job titles<br>specified by year; up<br>to 10 years|322|
|Road Haulage Industry|Transport, food,<br>accommodation<br>and ICT (transport<br>and storage)|6|All job titles<br>specified by year; 8<br>years|48|
|Salt ManufacturingIndustry|Manufacturing|7|NA|7|
|SecurityGuards|Across all sectors|1|8years|8|
|Sugar Industry (Agricultural)|Agriculture,<br>forestryand fishing|18|NA|18|
|Sugar Industry (Non-<br>agricultural)|Manufacturing|35|Many job titles<br>specified by year; up<br>to 6 years. Some<br>job titles further<br>categorized<br>accordingtograde|58|
|Tailoring Trade|Manufacturing|5|Learner specified by<br>year;5years|9|
|Tea Industry|Agriculture,<br>forestry and fishing<br>Manufacturing|32|NA|32|
|Travel Agents and Tour<br>Operators|Finance and<br>professional<br>services (admin and<br>support)|17|All except Trainee<br>and Watchman<br>specified by year; 4<br>years|62|
|**_Total_**||**_446_**||**_2003_**|

# **2 Data and Legislated Wages’ Trends**

We use a series of repeated cross-section data from the Mauritian labor force survey, known as the Continuous Multi-Purpose Household Survey (CMPHS), conducted between 2004 and 2014. The CMPHS was launched by Statistics Mauritius in April 1999. Since then it has been conducted monthly with the exception of 2000 when the survey was suspended to avoid overlapping with the Housing and Population Census.

We restrict our sample to include only employed individuals and those with non-zero reported earnings. Self-employed, own account and unemployed workers who have worked before are included in our analysis as the uncovered sector in our estimation of minimum wage effects. For this estimation, only workers who are in the covered RO sectors and the self-employed, employers and unemployed in or last employed in the RO covered sectors are considered.

We assign minimum wages to workers covered by 30 categories of remuneration orders and estimate employment and hours of work effects through the use of data related to two groups 7
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Our baseline sample period focuses on the effects of the earlier years of the AIA from 2007 to 2013 for total investment and cloud. The year 2013 is chosen as this is the first time for which questions on cloud use were included in the E-commerce survey. We assume zero adoption for all firms in 2007, consistent with the assumption of DeStefano et al. (2023), as this is before cloud computing arrived in the UK. In the robustness section we test for the use of the other time periods for which information on cloud use by firms is available, namely 2015, 2017 and 2019. Information on the use of big data analytics are collected for the years 2015 and 2019 and for AI in the year 2019. Again we assume zero adoption in 2007 for both. Worker-level estimation of the demand for data workers and total investment at the firm level takes advantage of annual data for the years 2007 to 2013 and 2007 to 2019 (inclusive).

Table 2 below provides summary statistics of the main variables and time periods we use in the main body of the paper. For the period up to 2013 our data show that 28% of firms in our sample use cloud, but that this varies across types of cloud service. For example, only 6% of firms use cloud for finance software, whereas 16% use cloud for storage of files. For big data analytics, 21% of firms use big data by 2015 (26% in 2019), while only 1.7% of firms use AI by 2019. Additional summary statistics for all firm variables for all time periods are available in Table A1, and worker summary statistics are in Table A2.

# **4 Empirical strategy**

To identify the effect of the AIA capital allowances on capital investment, cloud technologies, big data and AI we use a difference-in-differences (DID) specification with some minor distinctions discussed below depending on whether we model IT investment (available annually) or the technology variables (available in specific years). The structure of the difference-indifference regressions measure the outcome of firm _i_ in period _t_ before and after AIA, the introduction of the AIA allowance relative to the control group expressed as follows, where _Zit_ 13
which has an intuitive interpretation that closely connects to a similar expression in Eaton and Kortum (2002). The righthand terms express the overall economic size of the importer j and its preferences over goods. The lefthand fraction describes the extent to which the exporter is the lowest cost producer of the good, taking into account delivery costs, production costs, technology levels, and the underlying heterogeneity in varieties for countries. Under assumptions of balanced trade, the relatives wages around the world can further be determined.

Costinot et al. (2012) show that equation (S1.4) provides the foundation for estimating an ̃ k econometric equation of the form shown in specification (1) of main text: ln �x<sup>k</sup> ij� =<sup> ̄</sup> δij +<sup> ̄</sup> δj<sup>+</sup> θ ln( ̃zi<sup>k) + ̄εk</sup> ij<sup>,wherex ̃k</sup> ij<sup>represents“corrected” tradeflowsthatadjustforcountryopenness.</sup> Similarly, z ̃i<sup>krepresents observed productivity, given that not every country produces every good</sup> as it can import goods from other countries.<sup> ̄</sup> δij and<sup> ̄</sup> δkj<sup>arevectorsofexporter-importerand</sup> importer-industry fixed effects. Comparing these equations shows the basic function of these fixed effects. The importer-industry fixed effects control for the righthand terms about the importer and its preferences over goods. Importer-industry fixed effects also account for the denominator of the lefthand fraction, given that it is a worldwide aggregate for an industry. The exporter-importer fixed effects capture the numerator’s terms, which emphasize cost levels in the exporter and distances between the two countries. Under the assumption that the delivery cost term d<sup>k</sup> ij<sup>inthenumeratorcanbeexpressedinproportionatetermsoverthesetwovectors</sup> of fixed effects (e.g., d<sup>k</sup> ij<sup>= dij· dk</sup> j<sup>),specification(1)ofmaintextprovidesanunbiasedestimate</sup> of the θ parameter. An alternative assumption is that the residual differences in delivery costs after controlling for these vectors of fixed effects are uncorrelated with the focal productivity level z ̃i<sup>k.</sup>

# III Extended Data Development

## Labor Productivity Data

The UNIDO collects industry-level manufacturing statistics for The International Yearbook of Industrial Statistics and specialized publications on topics like development and competition. Researchers at the UNIDO supplement the data resources of the OECD with national records for non-OECD members, creating a unique global resource. The UNIDO’s stated objective is the compilation of internationally comparable and internally consistent series (e.g., variable definitions, accounting units, collection procedures).

Table S1a describes the 88 exporting countries included. Column 2 provides a count of the 3
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Riera-Crichton, D., Vegh, C., Vuletin, G., 2015. Procyclical and countercyclical ...scal multipliers: Evidence from OECD countries. Journal of International Money and Finance 52, 15-31.

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42
-4-

||Number of:|||P|G|X||||
|---|---|---|---|---|---|---|---|---|---|
||:countries:Con|stanty|N||:2|:||F|RSS|
||:<br>:|||P<br>|:<br>Y|V||||
|(b) Elimination<br>of outliers||||||||||
|or aberrant<br>countries||||||||||
|Cf. total sape (eq. 1)|69<br>:|||||||||
|(22) Total sample mInus||||||||||
|th country with|:|||||||||
|the largest<br>residual||||||||||
|In equatIon 1|68<br>0,937|: 0,611|: 0,,464 :|-0,037<br>|: -0,019|0.4|0,33|<br>6,165|<br>219,977|
|(without botiwona)||:(1,707)|: (2,385)**<br>:|<br>(3,07 )<br>|: (0,614)|(3,52 )*|:|||
|(23) Total sample ainus||||||||||
|the two countrls|:<br>:|||||||||
|with tho largest|:|:|:|||||||
|residuals in equatio|n :<br>::|||||||||
|1 (without<br>Syria and|: 67<br>1,826|: 0,534|: 0,404<br>:|-0,034<br>|: -0,04|: 0,4|0,36|<br>6,961|<br>190,509|
|Botswana)||: (1,557)|: (2,160)**<br>:|<br>(2,964)*|: (1,29M)|* (3,73)**||||
|(24) Total**sample**minus||:|||:|||||
|the thr _ countries|<br>:|||||||||
|with the largest|:|||||||||
|residuals In equatio|n:||:||:|||||
|1 (without<br>Jamalca,|: 66<br>1,064|: 0,65|: 0,395<br>:|-0,036<br>|: -0,03|: 0,4|: 0,37|<br>7,011|<br>246,669|
|Syria and Botswana)||: (1,955)*|*<br>: (2,199)**<br>:|<br>(3,216)*"<br>|<br>: (1,000)|: (3,695)*"||||
|(25)Totel**sample**minus|:<br>:|:||||||||
|the 4 countries wIth|:|||||||||
|the largest<br>residual|s ::|||||||||
|In equationI(with-|:<br>:||:|||||||
|out Lesotho,<br>Jamalca|, : 65<br>0,064|: 0,820|: 0,473<br>:|-0,037<br>|: -0,337|: 0,3|: 0,43|: 8,790|<br>270,422|
||<br>|256**|2774*|359*"|<br>(1311|(3790)*|:|||
|Syria and Botswana)|:<br>:|:,)|: (,) :|(,)|<br>,|: ,||||
|(26) Total**sample**minus|**:**<br>68<br>0,374|: 0,623|: 0,416<br>:|-0,03J<br>|: -0,007|0,9|0,45|: 10,337|: 242,638|
|Slngapor:||: (1,833|: (2,265)"*<br>:|<br>(3,354)*"<br>|<br>: (0,243)|: (5,172)*|:|||