rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9396

# **Abstract**

This paper uses Multiple Indicator Cluster Surveys data from the Republic of Congo and São Tomé and Príncipe to study the relationships between child stature, mother’s years of education, and indicators of early childhood development. The relationships are contrasted between two empirical approaches: the conventional approach whereby control variables are selected in an ad-hoc manner and the double machine-learning approach that employs datadriven methods to select controls from a much wider set of variables. Overall, the findings based on the preferable double machine-learning approach differ across the two countries depending on the measures of early childhood development and child stature (height-for-age Z-score and stunting) used in the analysis. Double machine-learning estimates for the Republic of Congo suggest that heightfor-age Z-score and stunting have a direct causal effect on early childhood development. In contrast, for São Tomé and Príncipe, no relationship is found. Thus, country-specific policy advice based on the relationships observed from data in other countries may be quite risky, if not misleading. Double machine-learning provides a practical and feasible approach to reducing threats to internal validity to derive robust inferences based on observational data for evidence-based policy advice.

This paper is a product of the Poverty and Equity 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 eskoufias@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
**FIGURE 6: GROWTH AND COMPOSITION OF PER CAPITA GDP, SENEGAL AND COMPARISON COUNTRIES**

<!-- Start of picture text --> Per Capita GDP Growth Rates, 1995‐2013 Composition of per Capita GDP by Sector<br>2.0<br>1.4<br>1.5<br>1.2<br>1.3 0.8 1.11<br>1.0 0.5<br>0.5<br>1.2<br>0.5 0.5 0.5 0.2 0.3 0.45<br>1.1 0.3 0.3 0.4 0.5 0.26 0.3<br>0.0 0.10.0<br>1.0<br>0.9<br>SSA WAEMU Senegal<br>SSA WAEMU Senegal Agriculture Industry Services<br>1995‐2005 2006‐2013 1995‐2005 2006‐2013 1995‐2005 2006‐2013<br>Cummulative growth index (1995=1)<br><!-- End of picture text --> Senegal’s poverty headcount fell by 20 percentage points during 1995-2005, nearly by 2 percent per annum, but by only 1.6 percentage points during 2006-2011. Although the slowing pace of poverty reduction is a natural consequence of the decline in growth from 4.5 percent to 3.4 percent, this is not a sufficient explanation. The additional explanation is that growth of services during the latter period was exclusive to a large segment of the workforce. The prominence contributions of communications and financial services—which are more capital and skill intensive—to growth during 2006-2013 is consistent with this trend.

The distribution of labor across sectors suggests that services-driven growth is relatively employment inelastic. Between 1995 and 2004, employment growth was overwhelmingly concentrated in the informal sector, which accounted for 97 percent of new jobs. Thus, while services led GDP growth in the capital-intensive formal sector, commerce and agriculture led job creation in the labor-intensive informal sector. These differences may explain the dominant share of the informal sector in overall employment growth, and the majority share of agriculture in total employment (table 7).

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|||||retire older vehicles and<br>purchase fuel-efficient new<br>vehicles|
|---|---|---|---|---|
|Jacobsen & van<br>Benthem<br>(2015)<br>United States|1993-2009|used vehicle scrap rates, the<br>gasoline price, and used car<br>resale value|IV approach relating<br>the natural logs of the<br>scrap rate and vehicle<br>price|Many of the gross<br>polluters in terms of smog<br>and ozone precursors tend<br>to be the very oldest<br>vehicles on the road|
|Chandra et al<br>(2010)<br>Canada|1989-2006|vehicle purchase and lease<br>data by model, year and<br>province as well as<br>characteristics of models<br>and of provinces|OLS|The average cost of<br>reducing carbon emissions<br>from the tax rebate<br>programs is estimated to<br>be $195 per tonne|
|Berensetenau &<br>Li (2011)<br>United States<br>**_Subsidies for Alternative Fuelin_**<br> <br>|1999-2006<br>**_g Infrastruct_**<br>|vehicle registrations in 22<br>Metropolitan Statistical<br>Areas (MSAs),<br>characteristics and number<br>of sales of virtually all new<br>vehicle models sold<br>**_ure_**<br>|Random Coefficient<br>Model<br>|Hybrid vehicle sales in<br>2006 would have been<br>37% lower had gasoline<br>prices stayed at the 1999<br>levels, and the effect of the<br>federal income tax credit<br>program is estimated at<br>20% in 2006<br>|
|Dimitropoulos<br>et al. (2016)<br>Netherlands|2012-2013|Survey|Panel latent class model|Policies that expand fast-<br>charging stations could be<br>an effective stimulus for<br>the early adoption of<br>BEVs, potentially saving<br>public spending for the<br>stimulation of the adoption<br>of electric vehicles|
|Li et al (2017)<br>United States|2011-2013|Quarterly EV sales and<br>charging station deployment<br>in 353 metro areas|OLS|The network effects of<br>charging stations on PEV<br>adoption are larger than|

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# **2.7 Estimation of Plant-Level Productivity, Marginal Costs and Markup**

To measure plants’ productivity, marginal cost and productivity, we estimate production functions using the panel data of manufacturing plants (Statistik Industri - SI) from 2008 to 2015. The details of the estimation procedure can be found in Section F of the Appendix. Unlike most empirical work using productivity estimates, we observe plant level physical output and input quantities and values. To deflate the total revenue of multi-product plants, capital and intermediate inputs expenditure (including energy consumption), we construct plant-specific output and input price indexes, as in Eslava et al. (2004). This is an advantage over contributions employing industry-level price deflators, which essentially assume that all plants within an industry face similar inputs’ cost and charge the same price.<sup>18</sup> Our approach allows us to obtain a measure of quantitytotal factor productivity (TFPQ). Exploiting quantity information, the latter reflects changes in pure technical efficiency, rather than in revenues.

A key challenge in the measurement and identification of productivity relates to the endogeneity of the firm’s optimal choice of inputs. We follow Ackerberg et al. (2015) and adopt a control function approach with the objective of proxying productivity known by plants’ managers, but unobserved by us, with energy expenditures.<sup>19</sup> Following De Loecker and Warzynski (2012), we adapt the methodology of Ackerberg et al. (2015) and allow plants’ exposure to robots to affect the expected value of future productivity, which in turn might determine differences across exposed and non-exposed plants and affect the estimation results.<sup>20</sup> After estimating the the parameters of the production functions industry by industry, we follow De Loecker and Warzynski (2012) and obtain a measure of plants’ marginal cost and markup, based on the plants’ first order conditions

> 18Foster et al. (2008) discuss the bias arising when using plant revenue deflated by industry deflators. De Loecker et al. (2016) extend the analysis in the contest of unobserved variation in input prices.

> 19We use energy rather than total intermediate inputs to edge against the potential input adjustment costs, which would be inconsistent with the assumptions on which the estimator is built. A detailed discussion is presented in Section F.

> 20Similarly, De Loecker et al. (2016) study the impact of trade reforms and include export dummies and import tariffs; De Loecker (2007) includes export quotas; Doraszelski and Jaumandreu (2013) include R&D expenditure, and Konings and Vanormelingen (2015) include measures of workforce training.

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import relatively lower quality digital goods. In the context of the Feenstra and Romalis (2014) framework, this positive relationship between import quality and income per capita is explained by reduced preferences for quality in low-income countries (relative to higher income countries). This effect via consumer preferences clearly dominates a positive supply-side effect: low-income countries tend to import less, sourcing only from the most efficient firms, which tend to sell higher quality goods.

Figure 4 reports average quality-adjusted prices (relative to the United States) across countries with varying income levels. The evidence in this figure reveals that, once quality differences are accounted for, digital goods in Africa are sourced at relatively similar prices to that of higher-income countries. The figure shows only a weakly positive relationship between income per capita and quality adjusted prices, but this relationship is somewhat noisy for AFCFTA nations, with several countries buying at higher prices than richer countries in other continents.

# **5 The effects of AfCFTA on digital goods imports**

In this section, we estimate the likely effects of AfCFTA on the imports of digital goods. AfCFTA is a groundbreaking trade agreement encompassing 54 African Union member states. It seeks to foster economic integration by progressively eliminating tariffs on 90 percent of traded goods, promoting trade in services, and implementing rules of origin to ensure preferential treatment of products. The agreement became operational on 7 July 2019 and officially started on 1 January 2021, with its permanent secretariat in Accra, Ghana. By 2030, the AfCFTA has the potential to create a single market of over 1.3 billion people with a combined GDP exceeding $3 trillion.

The agreement aims to eliminate tariffs on most goods and services, with varying timelines based on country development levels. The overarching goals of AfCFTA are to create a unified market, boost socioeconomic development, reduce poverty, and enhance Africa’s global competitiveness. It covers trade in i) goods and services, ii) investment, iii) intellectual property rights, and iv) competition policy.

Our main goal in this section is to estimate the potential growth in digital goods imports towards 2030, when the AfCFTA will be fully implemented. To this end, we draw on the model proposed by Hoekman, Ng, and Olarreaga (2002). This is a partial equilibrium model of the total demand for imported goods and the supply of exported products broken down to the 6-digit level of the Harmonized System (HS). The model simulates a counterfactual trade flow resulting from tariff liberalization and compares it with the base year (in our application, the year 2019). Import demand elasticities, for digital goods, were taken from our previous econometric 10
could be explained by its trend before the change of ownership. However, as discussed in the results section, we find that network access increased before nationalization; therefore, the differences in trends could only downward bias the estimated positive impact of the nationalization.

<<place table 3 about here>>

# **<<B>>** _Water quality tests_

The data on water quality come from URSEA. In 2004, URSEA created a water quality unit, in partnership with the chemistry department of University of La República, which was charged with monitoring the water supply system nationwide according to the guidelines of the World Health Organization. This unit conducts a number of water tests (in the distribution network) to measure the quality of the water provided to consumers.

Our units of observation are cities from Uruguay’s 19 departments. We use data for the 2004– 2009 period, but some observations are missing for some of the cities. As treatment cities, we use Maldonado, Pan de Azúcar, Piriápolis, Punta Ballena, Punta del Este, and San Carlos. We use the following control cities: Artigas, Canelones, Colonia, Dolores, Durazno, Florida, Fray Bentos, La Paloma, La Paz, Las Piedras, Melo, Mercedes, Minas, Montevideo, Pando, Paysandú, Progreso, Rivera, Rocha, Salto, San José, Atlántida, Tacuarembó, Toledo, Treinta y Tres, and Trinidad.<sup>17</sup> We use data for two different microbiological tests and two organoleptic tests that we will use as outcome variables because of their importance in terms of direct and indirect negative effects on health. The two microbiological tests are for fecal coliforms and pseudomonas aeruginosa. The two organoleptic tests are pH and cloudiness tests.

The first test indicates whether the fecal coliforms exceed the accepted maximum value. The second test indicates whether pseudomonas aeruginosa is present. In the case of organoleptic tests, the water quality unit reports the observed value. In the case of pH tests, the upper limit is 8 mg/L, and 14
WPS4200

# **PRICE STRUCTURE AND NETWORK EXTERNALITIES IN THE TELECOMMUNICATIONS**

# **INDUSTRY: EVIDENCE FROM SUB-SAHARAN AFRICA**

Finance, Economics and Urban Development (FEU) World Bank 1818 H Street N.W. Washington D.C. 20433 Tel: 202-473-4698 Fax: 202-522-3481 Email: aiimi@worldbank.org Abstract Many developing countries have experienced significant developments in their telecommunications network. Countries in Africa are no exception to this. The paper examines what factor facilitates most network expansion, using micro data from 45 fixedline and mobile telephone operators in 18 African countries. In theory the telecommunications sector has two sector-specific characteristics: network externalities and discriminatory pricing. It is found that many telephone operators in the region use peak and off-peak prices and termination-based price discrimination, but are less likely to rely on strategic fee schedules, such as tie-in arrangements. The estimated demand function based on a discrete consumer choice model indicates that termination-based discriminatory pricing can facilitate network expansion. It also shows that the implied price-cost margins are significantly high. Thus, price liberalization could be conducive to development of the telecommunications network led by the private sector. Some countries in Africa are still imposing certain price restrictions. Importantly, however, it remains a policy issue how the authorities should ensure reciprocal access between operators at reasonable cost.

# World Bank Policy Research Working Paper 4200, April 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> ¶ I am grateful to Cecilia Briceno-Garmendia, Antonio Estache, Laszlo Lovei, and Mark Williams for their insightful suggestions and comments on an earlier version. In addition, I acknowledge various telecommunications regulatory agencies in Africa, which kindly cooperated on my questionnaire survey on price regulation practices. I also thank Lorenzo Bertolini, Candy Jones, Anat Lewin, and Marta Priftis for their assistance in collecting relevant data.
# **_5.5. Student Learning vs. Parental Pressure_**

Finally, we examine whether the effects observed on schooling outcomes are indeed the result of teachers influencing results in response to increased parental pressure. While, we cannot test this hypothesis with the administrative data available, we did ask principals about this possibility in the May 2006 survey interviews.<sup>17</sup> More specifically, we asked principals about parents’ reactions to the possibility of their children repeating a year or receiving a very poor grade. Less than 3 percent of principals reported that parents demanded that undeserving children be allowed to progress. According to principals, the vast majority of parents (97 percent) accepted that their child was failing or that they received a poor grade. Hence, although AGE makes parents more demanding in terms of teacher attendance and attention to children’s learning needs, they do not seem to make them pressure teachers to change grades for undeserving students.

# **6. Conclusions**

Mexico’s AGE aims to empower parent associations to improve school quality. We have provided quantitative empirical evidence that AGE improved intermediate school outcomes, namely reducing grade repetition and grade failure by 4 to 5 percent. These results are important as Manacorda (2007) and Marshall (2003) show that repetition and failure are associated with poor test performance and a higher probability of subsequent drop out. However, a limitation of the study is that we only have crude measures of school performance, rather than more sensitive measures such as test scores.

> 17 Section 3 describes the random sample of principals used and provides a summary of the main findings stemming from these interviews on parental involvement in the school as a result of AGE.

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# **II. Data**

Sampling from the universe of registered businesses and following a uniform stratified random sampling methodology, the core WBES uses a standardized survey instrument to benchmark the investment climate of individual economies across the world.<sup>6</sup> The survey contains sufficient information to allow for firm performance analyses. The surveys also report detailed information on firm employment, age, industry, ownership, legal status, and the number of establishments.

As noted above, our analysis is designed to explain variation in firm labor growth. We use the percentage growth in the number of full-time employees over the two years prior to the survey as the dependent variable in our regressions because that information was asked of firms in a wider sample of countries than, for example, information about sales growth. In addition, the information provided by firm owners regarding their number of employees is likely to be more accurate than information about their sales, especially for smaller firms that either do not keep good accounting records or are reluctant to fully report their sales (e.g., because of potential tax consequences). For the 89 countries whose firms enter our regressions, the WBES surveys were conducted at different points in time between 2000 and 2009.

We use three firm characteristics as controls in our firm growth regressions: age, size as measured by the current number of employees, and the percentage of shares held by foreign owners. Both firm age and number of employees enter the regressions in logged form. We do not have strong priors about how these characteristics affect firms‘ labor growth, though if older firms are more likely to have reached their equilibrium size, they might have slower growth than younger firms. Percentage growth in employees might be greater for smaller firms, as they are starting from a low base of employees, but larger firms may have advantages, especially in terms of access to finance, that make hiring additional workers easier. It is also unclear whether firms with high shares of foreign ownership would expand employment more or less quickly than firms with higher shares of domestic ownership. Better access to finance by foreign firms would allow faster expansion, but a shorter-term focus on the part of those firms may limit firm expansion.

> 6 A detailed description of the sample design and sample frame can be found at: http://www.enterprisesurveys.org/documents/Sampling_Note.pdf.

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unit level.<sup>15</sup> For the cases of the continous variables, we estimated the DD model through Ordinary Least Squares. The OLS regressions were estimated with and without controls as well.

Finally, we performed five robustness checks to confirm the validity of the obtained results. First, we ran the same models, but for a previous period (2010-2012) to verify that these groups were actually moving in non-different (parallel) trends before the treatment. If the placebo difference-in-differences estimation for the period prior to the intervention is nonzero, the trustworthiness of the estimates comparing 2012 and 2014 would be low and it would be an indication that the estimates could be biased. Second, we implemented a placebo test for outcome variables that are ex-ante thought not to be affected by the program such as disability condition and access to drinking water. If the estimated effects for variables not supposed to be affected by the program is nonzero, this could also indicate that the obtained estimated effects might be biased. Third, we used adults 56-59 years old who do not receive contributory pensions as an alternative control group. We then looked for differences between the results obtained with the original control group and with the alternative control group, to check if results were sensitive to changes in the control group which could also affect the credibility of the estimated effects. As a fourth robustness check, we ran the same models without excluding the contributory pension receivers from the treatment and control groups to check if excluding this population explained the estimated effects. Lastly, we included the data from the states of Chiapas and Mexico City and used dummy variables in our model for each of these states to check if including these populations affected our estimated effects.

# **3.5.2 Treatment-on-the-treated Effects (TOT)**

As explained before, directly regressing the ENIGH-MCS variable that captures the self-reported PAM beneficiaries on extreme poverty would result in biased estimations due to endogeneity. In fact, the states and municipalities with higher poverty rates also have higher PAM enrollment rates. This is the case because the Ministry of Social Development selected localities with high poverty and high social deprivation rates as strategic locations to provide information and promote enrollment into the program. Thus, the mechanism to “encourage” eligible population to apply for enrollment was not random. While we could control for a wide variety of socioeconomic characteristics, results would not be convincing evidence of the causal effects of the program.

We define the binary variable that indicates whether an individual reported being a beneficiary of the program as _p_pam_ , where: Using the equation:

> 15The primary sampling units are formed by groups of houses with differentiated characteristics depending on the area to which they belong. Primary sampling units can be formed by groups of houses in one locality or in neighboring localities that have the same village size range.

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