rafmacalaba/data-use-mentions-extended huggingface dataset:
specifically, Calderon (2014) demonstrates an increase in income following the significant expansion of childcare in 2007.

<mark>Access to quality childcare also shows positive effects for children. Without adequate early childhood education, young children do not have the necessary skills to fully benefit from the education they receive at the primary level. Young children need sustained access to supportive, nurturing environments that provide a high degree of cognitive stimulation and emotional care throughout the early years. Compared to children who attend quality pre-primary programs, children who enter school without adequate preparation are more likely to have poor academic performance, repeat grades, and drop out of school. Quality preprimary programs are linked to life-long benefits for individuals and society at large. They reduce the need for remedial education or rehabilitative actions later on, including in terms of reducing the risk of incarceration and improving welfare in adulthood (Schweinhart et al 2005, Heckman & Masterov 2007, Reynolds et al 2001, Feinstein 2003, Currie and Thomas 1999). It must be noted, however, that children will only benefit from increased access to early childcare and education if the services being provided meet core quality standards (Schweinhart et al 2005).</mark> Demand for childcare services may be limited by a number of factors, including affordability, accessibility and quality (Mateo Días & Rodríguez-Chamussy 2016). Mateo Díaz and RodriguezChamussy (2016) cite evidence according to which the distance to a childcare center has been associated with the decision to enroll children (or not). At the same time, the demand for childcare services in Mexico does not seem to be exclusively determined by market variables (price, quality and quantity), but also by cultural factors and social norms. The notion that women are responsible for the care of children and the elderly is still widespread among Mexican households (Arceo Gómez & Santillán 2018). Arceo Gómez and Santillán (2018) analyze data from the ELCOS Survey (Encuesta 3
is extended to foreign currency deposits as well.

_Sources_ : Own survey of deposit insurers, IADI Survey: Slovenia (2003).

**Spain.** ( _Deposit Guarantee Fund, Royal Decree Law 4 & 18_ ) Spain has separate deposit guarantee funds for its commercial banks (established in 1977), savings banks (established in 1980), and credit cooperatives (established in 1982). The system is government legislated and privately administered. Each fund is jointly administered by their management commissions with eight members. Four members are from the Bank of Spain and the other four are from the member institutions. Deposits of financial institutions, public bodies, and insiders are not covered. Deposits in financial institutions from other EU countries are also covered. Membership to the Spanish scheme by branches of foreign banks – including the EU banks – is voluntary. The central bank can make limited loans to the Fund. The premiums are assessed annually with flat rate. In 2003 the rates were 0.6% for commercial banks, 0.4% for savings banks, and 1% for credit cooperatives. The coverage limits have taken the following values historically: 1977: 500,000 pesetas, 1980: 750,000 pesetas, 1981: 1.5 million pesetas, 1995: 15,000 ecus, 1996: 15,000 euros, 2000: 20,000 euros. _Sources_ : Own survey of deposit insurers, Garcia (1999), IADI Survey: Spain (2003), Institute of International Bankers (1997), Kyei (1995).

**Sri Lanka.** The scheme of Sri Lanka was established in 1987. It is officially administered and jointly funded. The central bank provided the initial capital and can advance funds. Membership to the scheme is voluntary. Deposits in foreign currencies are not covered. Deposits of the government, public corporations,

73
i

# **AN INTERNATIONAL STATISTICAL SURVEY OF GOVERNMENT EMPLOYMENT AND WAGES**

## _CONTENTS_

## Preface.

|**1. Scope of the survey and methodological problems**<br>**............................................**<br>**l**|
|---|
|2. The key findings on**goverrnent employment ......................................................**<br>**3**|
|**3.**The key findings on governm ent wages**......................................................**<br>**5**|
|4. Changes over the last decade**.....................................................**<br>**9**|
|**5. Determinants of government employment ...........................................
considering how some mechanisms are intertwined with multiple outcome groups and why some of our expectations are a priori ambiguous.

27. First, e-procurement is expected to lower intentional barriers to entry, by making it harder for public officials to create some types of barriers for unwanted competitors. E-procurement can automatically ensure compliance with procurement rules and hence reduce discretion. For example, automatically publishing notices about new tenders on a national website makes it difficult to hinder undesired competitors from accessing information about tender opportunities (Coviello et al., 2018). As e-procurement enables firms to download tender documents and submit bids online, it also makes intentional barriers obsolete that rely on enhancing the cost of bid submission––such as through physical violence, complex administrative procedures, or by denying receipt of tenders from unwanted bidders. This is particularly salient in Bangladesh, as extortion and the use of physical violence to limit competition were widespread under paperbased procurement.

28. E-procurement is also expected to lower intentional barriers to entry because it makes rent-seeking riskier. It produces an easily accessible electronic audit trail13 about the decisions leading to contract award (bid evaluation), facilitating managerial oversight and audit and hence increasing the risk of getting caught (Di Tella & Schargrodsky, 2003; Olken, 2007). This risk is salient in Bangladesh’s context because the CPTU uses e-procurement data for proactively monitoring procurement risks14 and has full authority to act on findings. This may motivate rent-seeking officials to go clean or to adopt strategies that are not easily detectable in electronic records.15 Consequently, if substitute strategies are unavailable, we expect e- procurement to unambiguously reduce intentional entry barriers.

29. However, e-procurement need not lower intentional barriers to entry if collusive agents16 find alternative strategies for raising these barriers.17 They may respond to reduced discretion or tighter monitoring in one part of the procurement process by substituting rent-seeking strategies away to other procurement phases (Dávid-Barrett & Fazekas, 2020). For example, once e-procurement makes it impossible to prevent competitors from bidding, connected or corrupt firms
could match 275 to the 1928 census.

# **4.1.3 Integration outcomes of refugees**

I measure the integration of refugees by examining different generations of refugees: (i) those born in turkey who were forcibly resettled in Greece at a different age (ii) those born in Greece to refugee parents, i.e. the second-generation.

**First-generation** I measure the socio-economic integration of first-generation refugees by comparing the educational attainment of refugees displaced at a different age, i.e., either when adults (above 15) or when children (below 15). I rely on the 1928 census to obtain the literacy rate of refugees at arrival, and focus on those older than 15 years in 1923. To measure the socio-economic outcomes of refugees at a later period, I unfortunately cannot draw on population censuses as no information on country of birth is available. The earliest census to provide this information is the 2001 census and I use the 10 percent extract provided by IPUMS.<sup>19</sup> I identify as refugees the 3,486 sampled individuals born in Turkey between 1908 and 1922, who represent about 9% of the population born in the same period. Reassuringly, the share of individuals born in Turkey drops to less than 2% among cohorts born after 1922, while the share of foreign-born (in other countries than Turkey) remains stable across cohorts and start increasing only after the 1950s (see Figure A.1 in the appendix). The 2001 census also allows to construct the rate of intermarriage between refugees and natives. Among the 2,102 refugee women in the sample, 293 report to be married with a currently co-residing husband – the other women being mostly widowed. When the husband belongs to the surveyed household, I can identify mixed refugee-native couple as instances in which the wife is refugee and the husband is born in Greece. Theoretically, although native-born, the husband could still be the child of refugee parents, which would hardly
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

# 1 Introduction

The Africa Infrastructure Country Diagnostic (AICD) has conducted extensive data collection and analysis of infrastructure in Africa, including the 15 countries of the Economic Community of West African States (ECOWAS). The results have been presented in a variety of continental reports covering different areas of infrastructure—information and communication technology (ICT), irrigation, power, transport, and water and sanitation—and different policy areas—including investment needs, fiscal costs, and sector performance.

The purpose of this regional report is to present the key AICD findings for the ECOWAS community. The main value in doing so is that it makes it possible to benchmark the infrastructure situation in the region against that of other African peers, to identify the main gaps in the regional infrastructure backbones, and to quantify the costs and benefits of regional integration, as well as their distribution across member states.

A number of methodological issues should be borne in mind.

First, owing to the cross-country nature of the data collection, there is inevitably a time lag in the data. The period covered by AICD runs from 2001 to 2006. Most of the technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Given the fast pace of regional integration, the snapshot presented here does not necessarily correspond to today‘s situation but rather represents the 2006 baseline against which subsequent progress can be measured.

Second, given the need to make comparisons across countries, indicators and analysis had to be standardized and made consistent. That means that some of the indicators may be slightly different from those routinely reported and discussed at the country level.

Third, the database on which the analysis is based was designed to give a national and continental picture of infrastructure, as opposed to an explicitly regional picture. But national infrastructure provides the basic building blocks for regional integration, and hence can be used to build
jointly with all the other variables.

# _4.2. The rationale of the explanatory variables_

Table VI summarizes each variable's rationale, along with the expected sign of their impact on the probability of a financial institution's distress and closure. Among the CAMEL-type variables, higher capitalization is expected to have a negative impact on the probability of both distress and closure, as the financial institution will be better able to absorb losses. A larger share of capitalization due to loan loss reserves is likely associated with more risky assets and can therefore be expected to increase the probability of distress. Conditional on the occurrence of distress, however, an

> **15** From the initial sample of 283 observations, five observation are discarded due to missing values.

> 16 Four observations were discarded due to missing values and 64 observations were not used due to no closure-event in Malaysia.

> **17** 104 observations are the 120 distressed institutions less the 16 Malaysian institutions that are not included due to no closure-event in Malaysia.

> **18** Note that we continue to use 1996-only data to analyze the closure and the closures conditional on distress decisions. As such, we are predicting in a sense which institutions were candidates for closure already in 1996.

10
Our results are consistent with recent work using census data, which also points to a critical role for large firms. In particular, Hsieh and Klenow (2012) show that an important difference in firm dynamics in India, Mexico, and the US relates to the ability for firms to grow large. Firms in the US are much larger, and weak life-cycle dynamics have constrained productivity in Mexico and India by an estimated 25 percent. Similarly, Haltiwanger, Jarmin, and Miranda (2012) show that young, fast-growing and large firms are the primary job creators in the US. Our work implies similar dynamics exist for trade, with highly productive firms growing quickly into large firms that dominate exports. Taken together this implies that jobs, productivity, export growth, and diversification all rely heavily on the ability of an economy to foster the development of large firms. Further research is needed in order to understand the process of how such superstars are born and what factors are most important in their evolution.

24
## constant.

For the rm-level shocks we estimate a correlation coecient of _ρa_ = 0 _._ 902 and a variance for the innovation term of _σa_<sup>2= 0</sup><sup>_._118.For the sector-level shocks we estimate a constant of</sup> _b_ = 0 _._ 318 , a correlation coecient of _ρb_ = 0 _._ 777 , and variance of _σb_<sup>2= 0</sup><sup>_._044.Once we have the AR(1) parameters</sup> we discretize the state space for _Aijt_ and _bjt_ and compute their Markov transition matrices following the approximation of Tauchen-Hussey. We dene an 8-point grid for _Aijt_ and two values for _bjt_ , high and low. The estimated probability of staying in the same state is 0.827 and the probability of switching is 0.172 (Table 1). More details about the estimation of the production function and stochastic process parameters are in on-line Appendix A.

Note that the rm survey covers only the manufacturing sector. For the non-manufacturing sector, which we want to include in the analysis given its importance in the overall economy, we use the production function and shock processes parameters estimated for the manufacturing sector.

# 3.2 Adjustment cost parameters

We now turn to the estimation of the vector of adjustment cost parameters Γ = ( _γ_ 1 _, γ_ 2 _, γ_ 3; _Ce, Cu_ ) . Recall that _γ_ 1 , _γ_ 2 and _γ_ 3 are the capital adjustment parameters: the xed cost, the convex cost and the irreversibility cost. On the worker side, _Ce_ and _Cu_ are the costs of moving between production sectors and in or out of non-employment.

The SMM is based on comparing a vector of empirical moments computed from rm and worker actual data, with moments computed from rm and worker simulated data. The simulated moments depend on the choice of Γ through optimal investment _Iijt_ and equilibrium labor allocation
**3**

In this paper, we focus on intra-household inequality in income-earning. This allows us to adopt a sharper test for mechanisms. It also allows us to hone in on gender income inequality, one of the most pervasive forms of social inequality. Through this lens, intra-household inequality can be affected by how (i) the impact of self-esteem differs across men and women, (ii) interacts with their partner’s self-esteem, and (iii) manifests when economic outcomes are constrained by scarcity and market failures in input provision, a particularly relevant topic in development settings.

Thus, the question of how noncognitive skills influence inequality – in our context, self-esteem and intra-household inequality in economic specialization specifically – rests on two axes. First, how does the interplay between the genders’ noncognitive skills influence the "general equilibrium", that is the household level? Second, what determines the sign of this interplay, i.e., whether spouses’ noncognitive skills are substitutes or complements? A key to answering this question rests on the fact that noncognitive skills, and self-esteem in particular, may affect outcomes by increasing one’s ability and willingness to control production inputs and thus determine how resources are allocated among household members (Engle (1990)).

This paper uses self-esteem as a measure of noncognitive skills since it is the main noncognitive skill analyzed in the economics literature. Most importantly, self-esteem has been found to be stable and strongly predictive of future outcomes by psychologists, making it suitable for study of impacts (Donnellan, Trzesniewski, Robins, Moffitt, and Caspi (2005); Orth, Robins, Trzesniewski, Maes, and Schmitt (2009) and references cited therein). Consequently, they are part of standard measures incorporated in large longitudinal studies such as the National Longitudinal Survey of Youth in the United States, the German Socio-Economic Panel and the Household Income and the Labour Dynamics in Australia Survey. Economists researching noncognitive skills (such as Cobb-Clark and Tan (2011); Heineck and Anger
the most connected firms are affected compared to the least connected or a random set of firms. The importance of the most connected firms increases non-linearly with the size of the negative productivity shock and decreases as firms become more substitutable. Finally, we quantify how important it is to also consider a firm’s indirect connectivity and find that under our elasticity and a negative productivity shock of 25%, the fall in GDP would be 2 _._ 56 _pp_ less if the most connected firms were allowed to operate during the lockdowns compared to only allowing the directly connected firms (counting only the number of direct buyers) to operate.<sup>8</sup> We see that as the shock gets larger, the difference in aggregate GDP between these two experiments rises, emphasizing the importance of measuring a firm’s indirect connections as well.

**Related Work.** Our paper contributes to two strands of the literature. First, we contribute to the literature on shock propagation and amplification through supply chains and production networks (Barrot and Sauvagnat, 2018; Carvalho et al., 2021; Peter and Ruane, 2022; Boehm et al., 2019; Korovkin and Makarin, 2020; Ferrari, 2022; Dew-Becker, 2023; Huneeus, 2018; Arkolakis et al., 2023).<sup>9</sup> A crucial parameter determining the degree of shock propagation through supply chains is the firm-level elasticity of substitution across suppliers within the same product category. Existing work has estimated firm-level elasticities of substitution between product categories or between domestic and foreign industries (Peter and Ruane, 2022; Carvalho et al., 2021; Boehm et al., 2019; Atalay, 2017). The challenge highlighted by this literature in estimating the elasticity across suppliers within a product category lies in the fact that most firm-to-firm datasets do not contain product-level (unit) prices from each supplying firm to a buying firm, or lack the required variation in such prices to estimate these elasticities.<sup>
We have two sources of data for aggregate factor incomes: data from National Accounts and data from the HBS. In our Russian data, capital‘s share of factor income is much larger in the National Account data than in the HBS (see table 3.1). This is typical. Ivanic [2004] mapped income from the Living Standards Measurement Surveys (LSMS) in 14 countries into factor shares and compared factor shares with the input-output tables in these countries. Capital‘s average share from the LSMS surveys was 21% of household income, but it was 52% of household income based on National Account information (based on the ―GTAP‖ data set).<sup>43</sup> We must produce a balanced Social Accounting Matrix in order to implement our integrated model, which means we must reconcile those differences. There are biases in both the collection of National Account and Household Survey data so that neither source is clearly correct. A key problem with the factor share data from the national accounts is that capital‘s share is calculated residually in the input-output tables. Then in sectors where labor payments are underreported, as in agriculture (where sole proprietors do not report their labor income and temporary workers are often informal workers) and services, the share of capital is biased up. Unprofitable sectors that receive state subsidies will be reported as labor intensive, despite the fact that in developing countries these are typically the capital intensive sectors. Harrison, Rutherford and Tarr (2003) have shown that this bias can lead to perverse reporting of which sectors are labor intensive in developing countries. On the other hand, income estimates from LSMS surveys are known to be less than income estimates from National Accounts. Deaton (2003) explains that one of the most likely explanations of the difference is that households fail to respond to the survey, and that the probability of non-response plausibly

> 43 Household income (net of taxes and transfers) in Russia exceeds household consumption for almost all households. Part of
employment, is measured using the same data.

The data on "total employed persons" is also used in the calculation of GDP per worker for 2007, which is used to help assess the fit of the "after" equilibrium solution. GDP per capita for 2007, which is also used in assessing the fit, is measured using population data from _Regional Economic Statistical Yearbook 2008_ .

Table A1 provides summary statistics for both the variables used in the estimation of the wage equations and the additional parameters used in obtaining the "before" and "after" numerical solutions to our model.

> 35 In the various provincial statistical yearbooks, the numbers of urban and rural employed persons exactly sum to the number of total employed persons at the provincial level.

36