rafmacalaba/data-use-mentions-extended huggingface dataset:
Finally, the barriers to human capital accumulation in any period can be decomposed from the composite frictions once the labor market frictions have been identified. **d. Occupation-specific productivity** i **:** The only parameters left to be calibrated are the productivity parameters and equilibrium conditions are used to pin them down. Given schooling choice i , relative propensities i , frictions i and AAi , and efficiency wages i , the average quality of workers is cc yy cc cc ss obtained. The average quality then pins down the labor supply i in each occupation and starting with ii (ii) ii(h) pp ττ ττ ww some initial guess of productivities, the total output is determined using the production technology. LLSS Once, total output is known, the productivities consistent with firm optimization are given by ii YY 1 i i = �<sup>LLSSi</sup> �σσ−1 σσ which are used to update labor supply till convergence is achieved. AA ii ww YY

# **A3.   Adjustments to the wage data**

As mentioned previously, I drop workers who earn less than threshold income and when data permit work fewer than threshold number of weeks. There are two additional model related adjustments that I make to the wage data.

**a.   Adjusting for changes in LFPR across periods for a cohort:** In the model, the LFPR of a cohort is

41
# Exports and Job Training<sup>_∗_</sup>

Paulo Bastos<sup>_†_</sup> Joana Silva<sup>_‡_</sup> Rafael Proen ̧ca<sup>_§_</sup> _Keywords_ : Export participation, skill upgrading, job training, wages, linked employer-employee data.

_JEL Codes_ : F12, F14, F16, F66, J24, J31, J38, O14.

> _∗_ We are grateful to Aguinaldo Maciente, Alejandra Martinez, Carlos Silva dos Santos, Daniel Lederman, Herlon Brand ̃ao, Laura Chioda, Renata Gukovas, Renato da Fonseca, Rita Almeida and participants at the Festchrift Conference in Honor of Professor Sir David Greenaway at the University of Nottingham for helpful comments and discussions. We are also grateful to the Ministry of Labor, the Ministry of Development, Industry and Foreign Trade and the National Confederation of Industry (CNI) for providing the administrative data used in the paper. Research for this paper has been supported in part by the World Bank’s Multidonor Trust Fund for Trade and Development and through the Strategic Research Partnership on Economic Development. We remain responsible for any errors.

- The World Bank. E-mail: pbastos@worldbank.org.

- Corresponding author: The World Bank, 1818 H Street NW, Mail Stop I9-900, Washington DC, United

- States. E-mail: jsilva@worldbank.org.

- _§_ The World Bank. E-mail: rproenca@worldbank.org.
greater volatility and the relatively high share of imported goods in total consumption, it seems plausible that in our setting, the foreign exchange rate plays a more important role in consumer decisions than the rate of inflation. As our theoretical framework in Section 2 highlights, consumers can partially offset the effect of higher inflation by shifting their consumption towards durable goods. We therefore predict that a consumer who expects higher inflation will increase the relative share of durable goods in credit card spending to insure against inflation risk. However, given that inflation in our setting is typically between 2 and 5 percent, this is unlikely to be very meaningful for the consumer’s overall finances. In contrast, when considering the purchase of tradable goods, such as consumer electronics or a car, the same consumer might be substantially affected by exchange rate fluctuations due to the magnitude in our setting. If the consumer, for example, expects a 20–25% exchange rate depreciation, the timing of such a purchase could amount to substantial savings.

To examine whether, in our context, changes in the exchange rate are in fact more salient to households than changes in the inflation rate, we exploit data on online searches and newspaper articles. Figure 3 plots data from Google Trends that tracks the frequency of online searches. These data have been used in several previous studies to measure public interest in specific 8
keyword search.

6 Even though many papers analyzed longitudinal data on maternal work, we excluded studies that only controlled for observables. One exception is the lagged dependent variable approach, which controls for the baseline value of the outcome variable. Although the lagged outcome variable is an observable, including it accounts for unobserved maternal or child characteristics that affected the earlier outcome, which reduces selection bias due to unmeasured child and family characteristics. However, if the study had controls for child development measures at baseline and these were from the same realm as the outcome variable, even if not exactly the same measure, we 10
(e.g. consumption aggregate or wealth index, shocks, household head gender, education, age), plot characteristics (e.g. crop loss, land area, irrigation).

There are some marked differences in household characteristics and agricultural outcomes between the Malawi IHS4 and IHPS 2016/17 samples. This may be the result of several factors. First, the IHPS is a panel whose sample households have been interviewed several times before since the first round of the IHPS in 2010. Respondents are likely familiar with the survey process and the survey instrument and may have become better at responding to questions. At the same time, those respondents may act strategically and take shortcuts in answering in order to reduce the time of the interview, as they are familiar with the questionnaire structure. It is also likely that the sample of agricultural households in the panel survey that we select for this analysis have been active in farming for an extended period of time, having been followed since 2010. Second, data on agricultural outcomes were collected in two rather than in one visit. This means, on the one hand, that the recall length for many agricultural outcomes of interest is cut in half. It may further lead to differences in recorded agricultural outcomes beyond what the recall length can explain. Unfortunately, we cannot distinguish between the recall effect, the effect of forming part of a long-running panel, and the effect of a post-planting visit beyond recall length with the IHS4 and IHPS 2016/17 data sets, though these issues would make for interesting future research.

# Empirical strategy

We use the following specification to assess the effect of the recall length on outcomes of interest: with Y� the outcome variable of household _i_ (or plot _i_ depending on the level of analysis), T� explanatory variable of interest (planting or harvest recall length in months, depending on the outcome of interest), X� a vector of control variables, γ an indicator for region, μ an enumerator fixed effect, and ε
WPS3822

# **Telecommunications Performance, Reforms, and Governance**<sup>*****</sup>

## **ANTONIO ESTACHE**

The World Bank ECARES, Universite Libre de Bruxelles _E-mail:_ _<u>aestache@worldbank.org</u>_ **ANA GOICOECHEA** The World Bank _E-mail:_ _<u>agoicoechea@worldbank.org</u>_ **MARCO MANACORDA** Department of Economics, QMUL CEP, London School of Economics _E-mail:_ _<u>M.Manacorda@lse.ac.uk</u>_

## **Abstract**

This paper assesses the effects of private capital and independent regulatory agencies on telecommunications performance by using cross-country panel data from 1990 to 2003. In general, we find that having independent regulatory agencies positively affects affordability and labor productivity, but negatively affects quality; while having private capital positively affects access, quality and labor productivity, but negatively affects affordability. However, reform policies affect developing and developed countries differently in some cases. We also find that governance plays an important role as it affects performance and interacts with reform policies.

## World Bank Policy Research Working Paper 3822, January 2006

_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._

* We are grateful to Jose-Luis Guasch, Paul Noumba Um, Stephane Saussier, Tomas Serebrisky, Lourdes Trujillo, Tommaso Valletti, David
complemented with data from the panel of ENCEL 1997-2000.

Absolute mobility in schooling is translated into 2.5 additional years on average for the group with lower exposure to PROSPERA and 3.7 additional years on average for youth who started receiving the program during early childhood, in comparison with the group that started receiving the program when they were no longer in critical periods for growth and development. For this outcome differences in gender are not as pronounced as in the case of height.

As a complement to the absolute mobility estimators in schooling, we estimated the simple differences in scores on the cognitive and memory test performed in ENCEL 2017. We do this because we do not have information about the provider from when the youth were 14 years of age.

These results show the same logic of the gradient associated with exposure to PROSPERA; consistent with the results observed for education and height. Here, the gains in both scores are slightly higher for males than for females (Appendix E). These results suggest that the gains in years of schooling are accompanied by gains in cognitive performance, although the latter cannot be measured in an intergenerational manner, due to limitations of the data.

The analysis by sub-population does not have a sufficient number of observations, resulting in a lack of common support in the matching estimator. For this reason, we present the estimation for the group of individuals who speak an indigenous language, without differentiating by gender. Table 8 shows that for the population who speaks an indigenous language the mobility gains in height are relatively lower than those estimated for the total of the population. This occurs in spite of the fact that the mobility potential we estimated in section VII.A was similar to that of the total population. In terms of mobility in schooling, the gains are greater among the population speaking an indigenous language, but the estimators for the cognitive and memory development present 29
.15***|0.01***|0.01***|
|Christian Share of|(0.001)|(0.001)|(0.005)|(0.007)|(0.000)|(0.000)|
|Population|||||||
|F-test|599.40|442.34|852.97|444.94|898.03|1053.12|
|Partial _R_<sup>2</sup>|0.913|0.927|0.950|0.949|0.976|0.981|

Notes: Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. _Female Labor Intensity_ ( _FLi_ ) is the average fraction of female employees in a sector at the fourdigit level of the US Census classification; _Fertility Rate_ is total births per woman; _Female Labor Force Participation (FLFP)_ is female labor participation rate (% of female population ages 15+); _Ratio of Female to Total Education_ is the ratio of the Barro-Lee measures of female to total educational attainment. All of 37 the variables are averages over the period 1962-2007, except for _Female Labor Force Participation,_ which is averaged over the period 1980-2007. Variable definitions and sources are described in detail in the text.
with the mode of transmission, estimates of informal flows using responses on transmission channels will be unbiased.

We employ the direct approach in using household surveys from Dominican Republic, El Salvador, and the Philippines, as well as report results from surveys for Armenia, Bangladesh, Guatemala, Moldova, and Uganda.

# _Shadow Economies and Developing a Model-Based Approaches_

To develop a structural model of remittances that can be estimated, we use insights from the literature on shadow economies. In general, the size of the informal economy is estimated using the relationship between variables that are correlated with the size of the informal economy, such as the currency relative to demand deposits or production, and variables create an incentive to go underground, such as the tax burden. The problem with this approach is that it can yield reasonable estimates of the growth of the underground economy, but some strong assumptions are needed to generate level estimates.

One of the most commonly used methods is the currency demand approach, developed by Phillip Cagan (1958). It uses the correlation between taxes and money demand to estimate the informal economy. The basic idea is that the shadow economy uses cash for transactions. The cash to current and deposit accounts ratio is then regressed on taxes and other conventional variables. Increases in the demand for currency that are not explained by standard factors, i.e. the residual plus that explained by increased taxes, reflect the shadow economy. Numerous criticisms have been leveled against the currency approach, including how to calibrate the model—in some base year 7
# **2 Data and Sample**

## **2.1 The Gender Equality at Home Survey**

Our analysis uses the individual level data from the Gender Equality at Home survey that was administered on the Facebook platform in July 2020.<sup>10</sup> The Gender Equality at Home survey is a collaboration between Facebook, the World Bank and other development partners to survey individuals on Facebook on issues related to gender equality and women’s empowerment. The partners developed a short survey questionnaire to collect data from Facebook’s general population of users. The questionnaire was designed to measure employment, beliefs and norms on gender, plus a number of key demographic questions (for example, gender, age, and marital status of the respondent) as well as time spent on work and domestic and care responsibilities, decision making and resource allocation across household members.

Since the survey was administered in July 2020, additional questions on the impact of the coronavirus (COVID-19) pandemic were also included. Since the survey was still early in the coronavirus pandemic it is less of a concern that survey responses were directly affected by the pandemic.<sup>11</sup> Certain survey modules were randomized across participants in order to mitigate survey fatigue among respondents such that no more than 30 questions were asked in total.

The survey was administered on the Facebook platform across 208 countries, territories, and islands. The sampling frame for this survey is the individual database of Facebook and it was administered to 461,748 respondents sampled across the globe from the target population. While 208 economies were surveyed, the sample considered in this paper is the 111 countries with sufficient sample size to conduct gender-disaggregated analysis as described further in section 2.2. The dataset is designed to reflect the Facebook user base rather than any specific national population, focusing on countries where internet access is widely available.<sup>12</sup>

## **2.2 Sample**

In this paper
3, this indicates that health and nutrition interventions can translate into meaningful education outcomes.

# **4.2 Specific cost-effectiveness studies**

## **4.2.1 Effectiveness and cost-effectiveness**

Next, we examine specific interventions to explore the degree to which aggregate patterns might parallel more granular ones or reveal underlying heterogeneity. Figure 5 shows results for absolute LAYS gained by intervention and country for the studies that include cost-effectiveness data. The top ten performers are: a combined intervention with improved pedagogy, para-teachers and targeted instruction in The Gambia (4 LAYS); the Campaign for Female Education (Camfed) program in Tanzania—a holistic program that includes scholarships and mentorship for girls along with school materials and training for teachers and parents (1.1 LAYS);<sup>13</sup> Tusome (the Kiswahili word for “Let’s Read”) in Kenya—a program that provides textbooks, teacher coaching, and teacher training (1.04 LAYS); building village-based schools in Afghanistan (0.74 LAYS); computer-assisted learning (CAL) in India (0.43 LAYS); community-based preschools in Mozambique (0.41 LAYS);

> 12In Appendix C we complement our data with data from the 2023 GEEAP report (Akyeampong et al., 2023) and compare impact sizes in Figure C2.

> 13CAMFED does not only provide financial support in its program; the breakdown by type of service provided by CAMFED in the paper does not reflect CAMFED’s main offering but rather illustrates how various program offerings might operate together to inform other programs.

19
and full health. Based on the HCI, Guinea was ranked 141 of 157 countries in 2017.<sup>1</sup> Poverty has increased from 49 percent (national poverty line) in 2002 to 55 percent in 2012, according to the most recent available national poverty survey. After the Ebola crisis, the poverty rate has probably further increased to about 60 percent in Guinea (World Bank, 2016).

Lack of economic growth largely explains the paradox between rich natural resources endowments and low level of human development outcomes and high level of poverty in Guinea. Indeed, since its independence in 1958, Guinea has experienced slow and volatile economic growth rates.<sup>2</sup>

> 1 The Human Capital Index has been recently developed by the World Bank. It quantifies the contribution of health and education to the productivity of the next generation of workers. For further information on the methodology, see Kraay (2018).

> 2 For further discussion on growth binding constraints in Guinea, see Mijiyawa (2017a).

2
and they are granted on a per depositor per institution basis. The scheme is privately funded and it extends to foreign currency and inter-bank deposits as well.

_Sources_ : Own survey of deposit insurers, IADI Survey: Bosnia and Herzegovina (2002).

**Brazil.** ( _Fundo Garantidor de Creditos-FGC, Resolutions 2197, 3024_ ) FGC commenced its operations in November 1995. The scheme is privately administered. Membership to the system is mandatory. The banks pay a premium of 0.3 % of the insured deposits. The system does not extend coverage to inter-bank deposits and the coverage limit is set at Reais 20,000. The EDIS was revised in 2002 but the coverage was left unchanged.

_Sources_ : FGC (2004), Garcia (1999), Talley (1998).

**Bulgaria.** ( _Law on Bank Deposit Guaranty, Deposit Insurance_ Fund) The deposit insurance scheme in Bulgaria was established in January 1996 based on Directive 94/19/EC of 1994. At the time, only deposits of physical persons were insured up to EUR 2,500. Due to the failure of 15 banks in the 1996 financial crisis, a blanket guarantee was applied to individual deposits and 50% repayment on company deposits. The Bulgarian Deposit Insurance Fund was established in early 1999. The coverage was raised to BGN 6,900 (EUR 3,528) in April 1998, to BGN 10,000 (EUR 5,113) in 2001 and finally to BGN 15,000 (EUR 7,670) in 2002. The scheme is jointly administered and the membership is mandatory. Insider deposits and deposits paying preferential interest rates are not covered. If the funds’ resources are not adequate, banks can be called to contribute an advance premium of 1.5% of insured deposits. The co-insurance was abolished in 2001. The fund has the right to borrow, including from the government in the last resort to receive donations and foreign assistance.

_Sources_ : Central European (1998),
higher fraction of Keralite households with members abroad depend exclusively on remittances for income than do households with members working elsewhere in India. This suggests that households with overseas migrants may rely more heavily on remittances for current consumption needs.<sup>14</sup> On the other hand, the origin household might treat the remittances sent back to it by a domestic migrant as a mixture of transitory and permanent income, and use them for both consumption and investment. The less the household expects its family member’s migration to be temporary, the less likely it is to view the transfers it receives as temporary income. Accordingly, the marginal propensity to invest domestic remittances is likely to be lower than the propensity to invest overseas remittances. Another related factor is that an individual who moves within India to work may, at a certain point, start remitting a smaller share of his or her earnings, because the growing prospect of settling long-term or permanently in the new domicile presents additional expenses. This reduction in domestic remittances would then limit the ability of the household back home to make investments comparable in size to the investments made by households receiving overseas remittances, even if the marginal propensity to invest domestic transfers were the same as for overseas transfers.<sup>15</sup> While the theoretical expectation is that households will invest a greater portion of overseas remittances than they will domestic remittances, the extent to which they actually do so is an empirical question that I investigate in this paper. Yet, migration is not simply a matter of the transfer and receipt of remittances. When a family member moves elsewhere for work, households no longer have access to his or her labor resources, insights, or acumen. If the migrating member used to make financial decisions for the family—for example, as the head of household—his or her departure may compel another member, such as the spouse or parents, to assume this responsibility. This, in turn,

> 14 The 1998 KMS data show that 38.
Germany, it would significantly boost its potential. Measures that would contribute the most are those that would enhance regulatory quality, government effectiveness, financial development, and human capital.

When decomposing economic growth into its drivers, the methodology adopted in the study does not assign a direct role to human capital, which only enters as a deeper determinant of labor, capital, and productivity. Researchers have also studied how reforms may affect human capital accumulation by treating it as an additional input into the production function. For example, Lutz et al. (2008) introduced a model where human capital by broad age groups enters production both as differentiated labor force inputs and through the absorption rate of new technologies, which depends on the interaction between human capital and the distance to the frontier. It would be interesting to extend the analysis to allow for a more direct role of human capital and to study its structural determinants and the impact of reforms. Another extension of this study would be to conduct a more systematic assessment of model uncertainty by adopting frontier methodology, which would minimize endogeneity and the bias arising from discretional selection of models.

# **References**

Adams, Z., Bishop, L. and Deakin, S. 2017. CBR Labor Regulation Index (Dataset of 117 Countries). Cambridge, UK: Centre for Business Research.

> Aiyar, S., et al. 2019. “Strengthening the Euro Area: The Role of National Structural Reforms in Enhancing Resilience.” IMF Staff Discussion Note SDN/19/05. International Monetary Fund, Washington, DC.

27
of birth (e.g. gender, parental status and politics), affect the perceptions of well-being when they play a role in determining income by means of facilitating positions or contracts.

**Perceptions of fairness are also correlated with higher levels of reported self-satisfaction.** For instance, using data on Italian workers, Tortia (2008) obtains that worker well-being is crucially influenced by fairness concerns. He obtains that self-satisfaction is positively affected by how fair workers perceive are the procedures

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