rafmacalaba/data-use-mentions-extended huggingface dataset:
should be large enough in order to minimize the bias arising from using pseudo panel data and not genuine panel data.<sup>25</sup>

- _Step 2._ Estimate the residual variance of the logarithm of the asset index within each cell of the pseudo panel corresponding to cohort _c_ at time _t_ . Practically speaking, we regress for each cell at the household level the logarithm of the asset index on a set of variables (including gender dummy, age and age squared, education dummies, household size, number of children under 5 years old, urbanization dummy or localisation dummies) and estimate the residuals. The residual variance over cohorts corresponds to the variance of the residuals of the previous regression.

- _Step 3._ Regress the residual variance on cohort dummies and a polynomial function of age. Then, draw the estimated age effects on a graph to obtain the age-profile of the residual variance.<sup>26</sup> Estimate the slope of this age-profile for each cohort _c_ ˆ 2

- which represents the estimated variance of the shocks faced by household,  _ct_  1 .

- _Step 4._ Draw a value  ~ _itc_  1 in the normal distribution with mean zero and variance ˆ 2

-  _ct_  1 within each cohort _c_ and combine it with the estimated coefficients of the observable characteristics to predict the vulnerability index _v_ ˆ _itc_ for each household _i_ at time _t_ belonging to cohort _c_ . For that purpose, _xitc_  1<sup>can be predicted</sup> _c_

- deterministically from _xit_ by incrementing age or assuming that characteristics are time invariant.

# **_Creation of a Pseudo-Panel_**

In order to have a look at the dynamic of asset-poverty, we regroup households from the DHS into homogeneous cohorts: households whose heads have the same date of birth (we define five-year cohorts), the same level of education (no education, primary and secondary and more) and the same place of residence (ten departments and urban
Learning from Developing Country Power Market Experiences: The Case of Philippines <!-- Start of picture text --> 2015 USD/MWh<br> 700<br> 600<br> 500<br> 400<br> 300<br> 200<br> 100<br> ‐<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Average spot price (2015 USD/MWh) Peak spot price (2015 USD/MWh)<br><!-- End of picture text --> Figure 5‐3 Evolution of yearly average and peak spot price (i.e. in the hour‐ahead market) in Philippines (in real terms).

Source: Own elaboration based on data collected by the World Bank.

Table 5‐2 Evolution of yearly average and peak spot real price (i.e. in the hour‐ahead market) in Philippines.

|**Year**|**Average spot**<br>**price (2015**<br>**P/MWh)**|**Average spot**<br>**price (2015**<br>**USD/MWh)**|**Peak spot price**<br>**(2015 USD/MWh)**|
|---|---|---|---|
|**2006**|7,154|116|179|
|**2007**|6,451|116|189|
|**2008**|4,292|84|145|
|**2009**|2,372|45|76|
|**2010**|8,250|169|269|
|**2011**|5,417|117|200|
|**2012**|7,684|
.<sup>20</sup> In the IV regression, there

> 19In particular, we created annual investment numbers between 1985 and 1996 by using linear interpolation, and then employed these interpolated investments, together with a depreciation rate of 7%, to bring the capital stock from 1985 forward to 1996.

> 20Although a central registry of formal employment contracts exists in Brazil, named Relação Anual de Informações Sociais (RAIS), we have chosen to use employment numbers from IBGE, which are based on a series of surveys conducted by IBGE. The reason is that the RAIS data may lack coverage of informal employment and the survey-based IBGE numbers are believed to reflect both formal and informal employment somewhat better.

35
Policy Research Working Paper 10785

# **Abstract**

This paper uses four decades of data on Brazilian municipalities to study the separate and joint impacts of highway and electricity infrastructure access on local economic outcomes. The identification strategy employs difference-in-difference estimators with staggered adoption design. The results show strong contemporaneous effects of electrifying municipalities that already have access to a highway, whereas electrification or highway provision alone may, at best, have no effect. Infrastructure investments also facilitated long-lasting structural transformation effects, with both types of infrastructure access spurring growth of the industrial output share.

This paper is a product of the Infrastructure Chief Economist Office, the Development Research Group and the Development Data Group, Development Economics. 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 hselod@worldbank.org, jsteinbuks@worldbank.org and bblankespoor@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
_The Global Information and Communications Technology Industry: Where Vietnam Fits in GVCs_ _Source_ : Country industry associations (Korea (FKII), Japan (JISA), Chinese Taipei (CISA), Nepal (NCA), Malaysia (PIKOM), Vietnam (VINASA), Bhutan (BICTTA)) and Vietnam Ministry of Information and Communication (MIC) for India and China. Note: Data compiled for ASOCIO report titled “Asia-Oceania Vision 2020 - Enabling IT leadership through collaboration.” Data for India and China compiled separately. Data for top-level management is unavailable from the MIC.

An important reason for Vietnam’s growing exports is its cost advantage over competitors in the region. As Table 8 indicates, Vietnam’s workforce is far less expensive than China’s or India’s. Entry-level computer programmers in Vietnam earn just 54% of what their Chinese counterparts make, and 36% of what entry-level Indian programmers make. According to data collected by the MIC, ICT workers in Vietnam have a lower attrition rate (5-7%) than workers in China (10-30%) and India (50%). In 2014, Vietnam surpassed India to become the second largest exporter of software services to Japan (China is still the largest).<sup>17</sup> While Vietnam will not compete on cost forever, human capital will be important in supporting upgrading efforts in the ICT services sector in the near term.

Cost competitive wages have driven growth in industry segments like software outsourcing and BPO. Software outsourcing is generally less capital-intensive than hardware manufacturing, creating opportunities for domestic firms to enter the market and grow quickly. Most of the larger software outsourcing firms in Vietnam are local firms serving the global market. Generally, these firms have their production sites in Vietnam and small sales and marketing presences in key markets around the world, especially the USA and Japan. Interviews with BPO firms confirm that Vietnam has attracted a great deal of work that was previously done in places like China, India and Eastern Europe.
investment in housing will be generated by one dollar of subsidy?<sup>23</sup> The focus on the leverage effect has been particularly important in the evaluation of slum upgrading and siteand-services programs (see for example Quigley, 1987).<sup>24</sup> Often, particular data from administrative files or surveys are not available, and the estimation of models like those used in the papers mentioned above is not possible. When adequate data are missing, other indicators of efficiency can be devised and they can vary on a case by case basis.

# _Visibility:_

Visibility refers to the degree of visibility of all costs of the subsidy for taxpayers and citizens. In order to measure visibility, one must first determine the actual subsidy cost and then determine how explicitly defined these costs are in the government’s budget. As a rule, costs are better known for subsidies on the State budget.<sup>25</sup> The cost of fiscal subsidies is often estimated because fiscal revenues are an important component of a country’s macroeconomic stability. In contrast, the cost of in-kind subsidies such as public housing or land are rarely estimated because the implied transfers are often implicit and do not appear on the main budget of the State or local authority. Thus, generally speaking, we can assess a decreasing degree of visibility to subsidies depending on their type and accounting method, as follows:

- subsidy on budget

> 23 There are many ways to estimate this type of impact, i.e., marginal or average impact, short-term vs. long-term impact, etc.

> 24 This may be attributed to the importance of the “merit good” aspect of housing for international institutions financing such programs.

> 25 However, this is often only true for direct costs, e.g., the amount of subsidy budgeted or distributed to beneficiaries during a given year. Indirect costs which are not the main preoccupation of governments, are rarely known without _ad hoc_ studies,
<!-- Start of picture text --> Routine Manual<br><!-- End of picture text --> <!-- Start of picture text --> 0,6<br>0,4<br>0,2<br>0<br>‐0,2<br>‐0,4<br>‐0,6<br>‐0,8<br>‐1<br>Argentina Uruguay Brazil Chile Peru Bolivia Dominican Mexico El<br>Rep. Salvador<br>NSE SE<br><!-- End of picture text --> Source: Own calculations based on Household surveys.

However, it should be noted that the tests do not allow us to identify a common trend across countries with respect to the speeds at which changes occur within NSE and SE. While in some countries a more rapid change is observed in the profile of tasks of non-standard workers compared to standard employment, in others, the opposite occurs.

It should be noted that within this general picture of non-standard employment trends in terms of their job profile, we found specific and, in some cases differentiating behaviors between part-time employment (Figure 14) and temporary employment (Figure 15).

Indeed, the tendency towards a more intensive profile in non-routine cognitive tasks is more evident in the case of temporary employment, where in all the countries analyzed, without exception, nonstandard employment presents this change in their profile. Additionally, while part-time employment shows heterogeneous behavior with respect to the trends of routine cognitive tasks, in the case of temporary employment there is a clear trend towards a less intense profile in this type of tasks.

Finally, the trend towards a less intensive profile of manual tasks is observed for most of the countries in both part-time and temporary employment although with exceptions in the two cases.

Page 22 of 51
Policy Research Working Paper 9810

# **Abstract**

Building on prior work that documented the impact of COVID-19 on firms in developing countries using the first wave of Business Pulse Surveys, this paper presents a new set of stylized facts on firm recovery, covering 65,000 observations in 38 countries. This paper suggests that: _One_ , since the outset of the pandemic, some aspects of business performance such as sales show signs of partial recovery. _Two_ , other aspects remain challenging, including persistently high uncertainty and financial fragility. _Three_ , recovery is heterogeneous across firms and more sensitive to firm-level attributes such as size, sector, and initial productivity than to country-level differences in the severity of the initial shock. In particular, larger and more productive firms are recovering faster, with implications for competition policy and allocative efficiency. _Four_ , the decline in jobs has been steeper during the initial shock than the expansion in employment during recovery, raising the risk of a “jobless” recovery pattern. _Five_ , the diffusion of digital technology and product innovation accelerated during the pandemic but did so unevenly, further widening gaps between small and large firms. _Six_ , businesses now have more access to policy support, but poorer countries continue to lag behind and appropriate targeting of firms remains a challenge.

This paper is a product of the Finance, Competitiveness and Innovation 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 agrover1@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
# **3. Data**

The Ministry of Education (MOE) in Guatemala has been constructing a comprehensive database of its students since 2009 (Adelman _et al_ ., 2018). Currently, all schools are required to provide complete lists of all students who are enrolled in each grade and the MOE assigns each an individual identifier to track them over time. Thus, these records are a longitudinal census of all students in the country for as long as they remain in the education system.

For the purposes of this study, these records enable the identification of students who drop out and the year in which they leave school. The data also include student-level demographics (gender, age, location); academic information (level, grade, final results of their academic year: promoted, not promoted, or withdrew); as well as school attributes, such as sector (public; private; cooperative; municipal), teaching modality (bilingual; monolingual), and school identifiers. We also have information on the municipality and department where the student attends school. There are available data for two pre-COVID-19 years (2018 and 2019) and two post-pandemic years (2020 and 2021), which corresponds to the most recent and complete school year data available from the Guatemalan MOE. We believe these records are well-suited to estimate the effects of differential municipal exposure to COVID-19 infections on educational outcomes for all students.

Table 2 shows descriptive statistics before and after the pandemic began. We have over 16 million observations across these four years. The table confirms the outcome trends shown in Figure 1, with higher dropout, promotion, and switching from private to public schools after the pandemic. Dropout is defined as between-year dropout: a student is observed in one school year but does not appear in the records the following academic year. Effective promotion is a binary indicator that is equal to one if a student is observed today in a higher grade than they were in the previous year and zero otherwise. School switching is a binary indicator
<!-- Start of picture text --> Figure 5: Matched plant species densities, Borgelt et al. (2022) vs. GBIF occurrence reports<br><!-- End of picture text --> <!-- Start of picture text --> ane! srt ‘i =<br>“a ww f<br>- ® >: a<br>. : hy &<br>6 ne<br>.<br>g NE Ly fe bee<br>GroupRankRank £ aeP ns . ;<br>meriri 2 ‘ ¥ Fi.<br>Li 3 :<br>a] 4 -<br><!-- End of picture text --> <!-- Start of picture text --> GroupRankRank<br>meriri 2<br>Li 3<br>a] 4<br>5<br>=e<br>ie<br><!-- End of picture text --> <!-- Start of picture text --> =e<br>ie<br>[ . te > *<br>Se<br>- YR oy<br>t/ - .<br>id<br><!-- End of picture text -->
19 pandemic. The specific question we explore here is how pre-pandemic technology sophistication - denoted as _technology readiness_ - affected firm performance during the early stage of the pandemic. Our analysis relies on a treatment effect mediation framework ( _11–14_ ) that has been used to disentangle the channels by which stimuli affect behavior. In our context, exogenous variation in pre-pandemic technology sophistication may affect firm resilience both directly, by facilitating operations and interactions with workers, customers, and suppliers in a context of an unexpected shock that abruptly reduce mobility and increase transaction costs among these actors, and indirectly via its effect on the adoption of additional digital technologies during the pandemic. Two conditions are necessary for the adoption of digital technologies to be a mediator in the relationship between technology readiness and firm resilience. First, that pre-pandemic technology sophistication is associated with an increasing use of digital technologies during the pandemic. Second, that variation in the adoption of digital technologies during the pandemic is significantly associated with firm resilience.

To estimate this statistical model, we use unique firm-level datasets that include firms in Brazil, Senegal, and Vietnam. The data provide granular pre-pandemic measures of the sophistication of the technologies used by firms in conducting seven key business functions - business administration, production/service planning, supply chain management, marketing, sales, payments and quality control - combined with information on whether firms started or increased the use of digital technology during the early stages of the pandemic; as well as their respective sales performance. Our analysis controls for several observable characteristics of the firm (e.g., age, size, exporting status, foreign ownership), including manager’s human capital (e.g., level of education and work experience) and managerial practices (e.g., incentives to workers and performance indicators) that could simultaneously influence digital adoption and performance of the firm. In addition, we use high-frequency measures of urban mobility and disaggregated sectoral information to control for the heterogeneity in the stringency of the measures adopted and the severity
between import independence and liberalisation but, interestingly, the estimated overall energy independence index is more significant than the independency index of the electricity sector alone. The study fails to detect significant interdependence between environmental commitment and the choice of liberalisation model.

Ruffin (2003) reports an econometric study of the institutional determinants of competition, ownership and extent of reform (dependent variables) in electricity sector restructuring. The institutional determinants (explanatory variables) used are different measures of judicial independence, distributional conflict, and economic ideology. The study uses a cross-section OLS regression analysis of a set of models with observations of up to 75 developed and developing countries that undertook some steps toward electricity reform during the 1990s. The study finds that the relation between judicial independence on the one hand, and competition and ownership on the other, is ambiguous; i.e. the coefficients are often insignificant or, when significant, their sign shifts across models. The results also suggest that greater distributional conflict is significantly correlated with a higher degree of monopoly, while in ownership models, the coefficients are mostly not significant. Moreover, the results indicate that the relation between economic ideology favouring competition and private ownership is generally positive and significant. Control variables, such as parliamentary control over government or per capita income, do not add much to the results.

In addition, Ruffin (2003) uses institutional explanatory factors with the electricity reform scores (as the number of reform steps taken) reported in ESMAP (1999) which reflect the extent of reform, including competition and privatisation. In these models, judicial independence shows a positive, though not always significant, relationship with 13
As a measure of well-being we will use household consumption for 1991/92 (GLSS-3), 1998/99 (GLSS-4), 2005/06 (GLSS-5) and 2012/13 (GLSS-6). In that, we depart from the literature using income as a measure of well-being. Our choice is mainly motivated by measurement issues, which play a very relevant role in the case of Ghana and have to do with the quality of the income measures that one can obtain from the GLSS data. In economies where agriculture is an important and established sector, consumption has indeed proven preferable to income because the latter is more volatile and more highly affected by the harvest seasons, so that relying on income as an indicator of welfare might under- or over-estimate living standards significantly (see, for instance, Deaton and Zaidi, 2002, and Haughton and Khandker, 2009). On the theoretical ground, as consumption gives utility to individuals, the analysis of its distribution should be the most natural approach to study wellbeing. Income matters insofar as it gives access to consumption, which is the ultimate source of individual welfare. Consumption is a better measure of long-term welfare also because households can borrow, draw down on savings, or receive public and private transfers to smooth short-run fluctuations. The GLSS collects sufficiently detailed information to facilitate estimates of the total consumption of each household. It relies on consumption per adult equivalent<sup>1</sup> to capture differences in need by age and economies of scale in consumption. Scales of consumption by age and sex are computed by the Ghana Statistical Service.

The GLSS is based on a two-stage (non-stratified) sample design. Therefore, when the data are analyzed, sampling weights are used to account for the survey design. Besides, to enhance comparability of consumption data over the four waves, all expenditures have been deflated across both space<sup>2</sup> and time and expressed in 2005 constant prices – as well as converted, when necessary, from Ghanaian second
coefficient on labor intensity is positive and statistically significant at the 10% level in one of the two specifications. This indicates that the NREGS, if anything, increases the labor intensity of crop portfolios (c.f. cols. (1) and (2)). The coefficient on cost intensity is also positive, suggesting that households are able to spend more on their agricultural production. Again, only one out of two specifications is statistically significant at conventional levels (c.f. cols (3) and (4)). I can therefore not rule out the possibility that the observed

> 43This is to exclude all households from the sample that - either because they consider it socially undesirable or because they have other means of risk coping - would probably never register with the NREGS.

> 44Both measures are based on the crop-wise Cost of Cultivation Statistics, published by the Ministry of Agriculture. See appendix B for more details.

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