rafmacalaba/data-use-mentions-extended huggingface dataset:
areas. Since agricultural households predominantly reside in rural areas where poor reliability of the mobile network and electricity is more common, they likewise may be underrepresented.

The level and nature of nonresponse can vary substantially depending on the context as well as the sampling approach adopted for the phone survey. Recontact surveys generally have higher response rates relative to RDD and telecom list-based phone surveys. One reason for this is that sampled units in recontact phone surveys have already been previously successfully contacted and interviewed and have an established relationship with the surveying agency as well as familiarity with being interviewed (relative to RDD and telecom list-based respondents). Furthermore, with RDD generated numbers are often unassigned or non-working, and survey practitioners are unable to tell in advance whether a number belongs to a household or a firm. For example, in an Ebola survey in Liberia, 214,823 calls to RDDgenerated numbers were made with only 24,000 of the numbers connecting; while in Ghana over 1 million RDD numbers were needed to get 16,003 connections (a connection rate of only 1.5%) (Himelein et al., 2020; Maffioli, 2020). Once a frame of phone numbers known to be assigned and working is established, response rates in RDD-based phone surveys are much higher, though usually still lower than recontact surveys (Henderson and Rosenbaum, 2020). IPA’s Recovr surveys, which relied on random digit dialing of phone numbers achieved response rates of between 4 percent in Mexico and 59 percent in Burkina Faso, with an average response rate of 28 percent (IPA, 2020). By contrast, some of the World Bank HFPS (recontact) surveys relied on phone numbers from previous LSMS-ISA surveys and achieved response rates ranging from 60 to 93 percent at an average of 74 percent (Gourlay et al., 2021;Table 1).

_Table 1. Response rates in round 1 of IPA Recovr surveys based on RDD (top panel) and HFPS surveys based on contact information from previous surveys (bottom panel)
, a reduction in exposure to same-race peers may lead workers to move to a different job. The same prediction arises in related theories of organizational demography (Pfeffer, 1983). Reduced similarity along demographic characteristics can entail lower levels of cooperation and communication, hinder social integration within a group, and weaken mentoring relationships and informal support networks among coworkers (Hoffman, 1985; Ragins, 1997; Tsui and O’Reilly III, 1989). Thereby, a decrease in the presence of same-race peers may lower job satisfaction and, ultimately, reduce retention at the firm (Jackson _et al._ , 1991; O’Reilly III _et al._ , 1989; Riordan and Shore, 1997).

We study the effects of a sudden change in the racial makeup of coworker peer groups on worker retention. Using matched employer-employee data from Brazil, we exploit exogenous shocks to the composition of coworkers that arise from unexpected deaths of workers from different racial groups. We compare the retention of incumbent workers in peer groups that encounter the death of a non-white worker with those confronted with the death of a white worker. Causal identification thus relies on the race of the deceased worker being independent of all other factors that drive incumbent workers’ retention. We check that, after we control for the number of non-white and white workers before the death, incumbent characteristics do not 1
.95 12<br>1.5<br>0.90 10 1.0<br>8 0.5<br>0.85<br>6 0.0<br>0.80 -0.5<br>4<br>0.75 2 spread in growth rates -1.0-1.5<br>0.70 0 -2.0<br>6% 7% 8% 9% 10% 5% 6% 7% 8% 9% 10%<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> _Source:_ Author estimates using data defined above 19
. These results demonstrate that the relationship between vulnerability and IPV can be very different if estimated using anonymous list experiment IPV data. In Rwanda, it also demonstrates that ACASI’s added privacy does not address the reasons vulnerable women are misreporting.

In Rwanda, we also observe a similar misreporting pattern based on a respondent’s perception of unequal village gender norms, which could proxy for shame about IPV or fear of potential repercussions if a woman’s survey answer were to be discovered. When a woman perceived that village gender norms were unequal, she was more likely to report IPV under the list method, where her answer is anonymous, than she was under the face-to-face or ACASI methods. Specifically, in Rwanda, a one standard deviation 15
|72|**83**|86|
|Rural road network condition [c]|% in good or fair condition|53|**56**|65|
|Classified paved road traffic|AADT|1,131|**1,033**|2,451|
|Classified unpaved road traffic|AADT|57|**60**|107|
|Primary network overengineering|% of primary network paved with 300 AADT or less|30|**34**|18|
|Primary network underengineering|% of primary network unpaved with 300 AADT or<br>more|13|**7**|20|
|Perceived transport quality [d]|% firms identifying transport as major business<br>constraint|28|**23**|18|

_Source:_ AICD Road Sector Database of 40 Sub-Saharan African countries.

a. Total network includes the classified and estimates of unclassified and urban networks.

b. Main network for most countries is defined as a result of adding the primary and secondary networks.

c. Rural network is generally defined as the tertiary network and does not include the unclassified roads. d. Source _:_ World Bank—IFC Enterprise Surveys on 32 Sub-Saharan African countries. GIS = geographic information system; AADT = average annual daily traffic.

## **Achievements**

During the ’90s the government initiated several institutional reforms and projects to rehabilitate and maintain road infrastructure in selected priority districts and corridors, easing transportation bottlenecks. After overcoming major hurdles—such as insufficient investment in rehabilitation and maintenance, and lack of local human resources sufficient to properly carry out road projects—and reforming the institutional and policy environments, Mozambique managed to establish a large road-infrastructure base.

Mozambique passed several institutional reforms in the early 2000s. The reforms included the implementation institutional and financial regulations, the creation of an interministerial road commission to coordinate government efforts, the establishment of an autonomous, dedicated ―road
men must command at least 27% (the GSS impact on teacher wages) higher wages then women. If they didn't, then private schools could setup in villages without a GSS by hiring (local/non-local) men rather than women as teachers. Andrabi et al. (2008) show that men (with the same observed characteristics) indeed earn 33 percent more than women, suggesting that men do not oer a viable teaching alternative.

Second, the entrepreneur could try to setup a school with a larger initial class-size in order to pay for the greater cost of hiring a male teacher. However, this has the trade-o of lowering quality. A natural constraint here is that student performance in the private schools must exceed that in the (free) public schools. Andrabi et al. (forthcoming) uses GMM methods together with children who switch school types to show that the yearly value-added of private schooling is around 0.25 standard-deviations. Although the estimates from the experimental literature on class-size reductions vary somewhat, a number of studies suggest gains of 0.2 to 0.3 standard deviations due to a reduction of four to ten students (Angrist and Lavy 1999, Krueger 1999, Muralidharan and Sundararaman forthcoming). Given median wages and school fees in Punjab, to generate enough revenue to cover the 33 percent higher wages of a male teacher, the school would need a class size that is seven children more than the median private school. This suggests that the quality drop from the increase in class size required to hire male teachers would almost entirely oset the private school advantage in these villages and hence not be viable. In other words, parents would choose public schools instead if the private school had a larger class size.

Third, the entrepreneur could set fees 33 percent higher than the current levels. Using data from Pakistan, Carnero et al. (2010) structurally estimate the elasticity of private school market shares to fees and nd that a 1 percent increase in prices reduces
variation across time and products identify the effects of NTMs on the performance of firms more cleanly than in the case of tariffs. However, it is also plausible to conjecture that the government may indeed use some NTMs to protect domestic producers from import competition, which would threaten the validity of the analysis. We cannot rule out this hypothesis, as we do not observe the real motives of the Indonesian government in setting NTMs. Hence, in the Section 3.1 we discuss several ways in which we address potential threats to the identification.

# _2.2. “Virtuous”_ vs. _“Protectionist” NTMs_

NTMs are highly heterogeneous measures with different aims and likely different effects. To guide our analysis, we make one central distinction between NTMs. We ask whether they are likely to breach a key principle of WTO rules, i.e. not restricting trade unnecessarily (Cadot et al., 2012). While NTMs’ _raison d’etre_ is to increase domestic welfare by addressing market failures, they can also distort trade unnecessarily in the process of fulfilling a legitimate policy objective, or even without fulfilling one.

Establishing whether a measure is strictly necessary to achieve a “legitimate policy objectives” is challenging, as policy-makers may have different definitions - even within the same country - of what “legitimate” means. One hint on its “legitimacy” is given by how widespread the application of the measure is across countries. An NTM that is used by most countries is more likely to be necessary than one that is used only by a handful of countries.

We focus on OECD countries, which tend to have more transparent trade policy, and compute the share of OECD countries that apply each of the 2-digit NTMs applied by Indonesia during the period of our analysis. We take data from UNCTAD’s _TRAINS_ dataset on NTMs applied by 34 OECD countries in 2015.

We restrict the analysis to the most common groups of applied NTMs in Indonesia, i.e. SPS, TBT and INSP. We
and skilled birth attendance is at 53%.<sup>9</sup> <mark>As per verbal autopsies and household surveys, two-thirds of total maternal deaths occur during the postpartum period, but postnatal care visits are still low at 65%.</mark><sup>10</sup> <mark>There are also</mark> persistent inequities in service utilization and health status among different socio-economic groups. Health indicators and service utilization, for example, are relatively better among the rich, urban and educated groups compared to their counterparts.<sup>11</sup> For 1
11 international trade relations (e.g., Balassa 1979). It relates the importance of each MENA country as a supplier of agricultural and other products to the world market (and alternatively to markets in MENA) relative to all competing exporting countries. In symbols, the indicator is computed as

# _International Trade:_

RCAj(i,w) [Xj(i,w)/ TX(i,w)) / [Xj(w,w)/TX(w,w)], (1) and

# _Intraregional Trade:_

# RCAj(i,m) = [Xj(i,m)/ TX(i,m)] / [Xj(w,m)/TX(w,m)I,

(2) where Xj(i,.) represents exports of commodity j by country i to the world (w) or MENA (m), Xj(w,.) represents exports of commodity j by the world (that is, exports of the commodity by all competing producers) to the same two markets, and TX(i,.) and TX(w,.) represent total exports of all commodities by country i and the world, respectively. Broadly speaking, RCA values greater than unity indicate comparative advantage, and RCA values less than unity indicate comparative disadvantage. 1 4

# International Trade

The exceptional comparative advantage in crude petroleum of the majority of countries in the Middle East and North Africa is apparent in the computed RCA statistics in Table 5. Although many MENA countries export substantial amounts of manufactured products, comparative 14 RCA values reflect not only fundamental factors such as natural resource endowments but also tradedistorting factors, such as foreign and domestic protection and also foreign and domestic subsidies to production and trade. Thus, for example, the RCA values computed for MENA trade in agriculture will reflect trade-distorting farm policies in the major industrial countries and the MENA countries themselves, including pricing of scarce water resources for irrigation and
costs (based on Olson 1965) and affects the per capita costs and benefits of agricultural policy, which, in turn, affects political outcomes in the presence of voter information costs (based on Downs 1957) or when political activities are proportional to the size of potential policy costs and benefits (Swinnen 1994). Recent papers in the media economics literature claim that the mass media can play an important role in public policy by altering these political economy mechanisms (Stromberg 2001, 2004a; Kuzyk and Mc Cluskey, 2006). Oberholzer-Gee and Waldfogel (2005) argue that the link between group size and political mobilization depends on the structure of media markets. In a series of influential papers, Strömberg (2001, 2004a) has shown that competition among mass media outlets leads to the provision of more news/information to large groups such as taxpayers and dispersed consumer interests, altering the trade-off in political competition and thus influencing public policy. He refers to this outcome as “mass media-competition-induced political bias.” The purpose of our paper is to evaluate whether the mass media have an impact on the political economy of agricultural policies by exploiting taxation and subsidization data from 69 countries that were observed from 1960 to 2004. The paper also contributes to an emerging body of literature analyzing whether the diffusion of free and independent media are key ingredients in more efficient public policies. Besley and Burgess (2001, 2002) use a political agency model to demonstrate that a more informed and politically active electorate increases the incentives for a government to be responsive. Prat and Strömberg (2005) find for Sweden that people who begin watching commercial television (TV) news programs increase their political knowledge and participation. Overall, this and other evidence (such as Besley and Burgess 2001, 2002; Francken et al. 2009, 2011; 4
# **1. Introduction**

The recent empirical literature on equality of opportunity (EOp) has provided a significant body of evidence on the extent of inequality of opportunity in different countries. See Brunori et al. (2015) for a first assessment of the existing evidence and Ferreira and Peragine (2016), Ramos and Van de Gaer (2016) and Roemer and Trannoy (2015) for methodological and conceptual issues related to the measurement of EOp.

A common feature of the existing literature is the static approach. Almost all empirical analyses use income distribution at a given point in time as the relevant distribution of individual advantages and are limited to computation of inequality of opportunity as a snapshot for a given country or set of countries.<sup>1</sup> This paper instead is concerned with the evolution of inequality of opportunity, i.e. with a dynamic approach. In addition, the time variation of EOp allows to study its main determinants. By so doing, we move the research on EOp a step forward and propose and test a (simple) empirical model that can explain the generation of inequality of opportunity in a given economy.

There are three different ways to analyze the evolution of inequality of opportunity, which correspond to three different concepts of inequality dynamics: (i) inequality measured across repeated snapshots of the population (repeated cross-sectional analysis); (ii) inequality measured along life courses (longitudinal analysis); (iii) inequality measured across generations (cohort analysis).

While analysis (ii) requires the availability of a rich longitudinal data set containing information of individual incomes and circumstances over the entire life cycle of the individuals, the analyses (i) and (iii) can be potentially carried out by using repeated cross-section surveys, hence are much less data intensive. This is the reason why in the present paper we focus on analyses (i) and (iii). See Aaberge et al. (2011) for an analysis of long-term inequality of opportunity along
The dataset comprises a variety of different cross-sectional and time series information. Inflation data (CPI) and import and export figures for both the CFA zone and the trading partners from 1999 to 2004 were obtained from the International Monetary Fund’s _International Financial Statistics_ . Monthly bilateral exchange rates of the CFA vis a vis its trading partners were obtained from FX history, the largest foreign exchange database on the Internet. The site gives monthly exchange rates of any two currencies in the world as an average of all daily rates from 1997 to 2005. Data on terms of trade, defined as export prices divided by import prices, were obtained from the World Bank’s _World Development Indicators_ . Trade weights for the two zones were calculated based on aggregating individual country export and import data from the IMF’s _Direction of Trade Statistics_ and were determined by taking the share of the zone’s trade with each trading partner. Information on GDP and other relevant macro variables is from the World Bank’s _World Development Indicators_ . Data on aid flows were obtained from the World Bank’s _Global Development Finance_ database.

## **C. PPP APPROACH**

### **1. BILATERAL AND MULTILATERAL EXCHANGE RATES**

The first step of the analysis is the computation of the bilateral exchange rate comparing the price of a representative production or consumption basket in UEMOA and CEMAC with the price of a representative basket in a foreign country measured in the same currency.

### (1) BRERdc   = Edc * PGf / PGd

where Edc is the index of the nominal exchange rate, defined as the units of domestic currency per unit of foreign currency, PGf is the foreign price index, and PGd is the domestic price index.

The second step of the analysis is to calculate the real effective exchange rate as a geometric weighted average of the bilateral real exchange rates with each of its main trading partners or competitors.

# (2) REERdc = Π (Edci *PGf / PGd )<sup>_wid_<
# 1. Introduction

Expanding access to electricity and water are key development priorities, closely related in the regions where access gaps are most pronounced, such as in the case of Sub-Saharan Africa (SSA). The World Bank estimates that currently, 600 million people in SSA lack access to electricity, creating significant barriers to health care, education, productivity, digital inclusivity, and ultimately job creation (IEA, IRENA, UNSD, World Bank, WHO., 2024). Those barriers are compounded by a lack of access to clean water: 400 million people in SSA lack access to safely managed drinking water services (UNICEF, WHO, 2022). Progress in photovoltaic technologies has enabled a lowering of costs (Chandel et al., 2015; Global Solar Atlas, 2024) that can be a game changer in expanding access. Multilateral development partners such as the World Bank and the African Development Bank Group are partnering to provide at least 300 million people in Africa with electricity access by 2030. The expansion of photovoltaic energy at scale will also enable an expansion of solar pumping for irrigation.

This is promising for SSA where less than 5 percent of agricultural land is irrigated, and less than 7 percent of irrigated lands uses groundwater (Rodella, Zaveri and Bertone, 2023). This could also present challenges in terms of impacts on groundwater-dependent ecosystems (GDEs) and overexploitation if access to groundwater resources is not appropriately controlled, a scenario welldocumented in regions such as South Asia and the Middle East (Lezzaik et al., 2018; Mukherjee, 2018).

There is growing recognition of the importance of those GDEs, from their socio-economic to their environmental value, including for carbon storage (Esteban and Dinar, 2016; Mendonça et al. 2017; Murray et al., 2003, 2006; Rohde et al., 2024). For instance, Mendonça et al. (2017) estimate that perennial lakes, mainly GDEs, trap some 0.33 billion tons of CO2 per year, corresponding to about 1%
Real wages of Formal Sector Workers (1960-91)**

<!-- Start of picture text --> i 300..<br>250 .<br>o200<br>50<br>0 II Iii I _+ ,.j I , _ I ,I ,I , IA+,<br>_~~ _ ~~~~ _ _ . _ _ 0(0 I<br><!-- End of picture text --> _Source: adaptedfrom ISSER, 1994, Figure 8.2, p. 159 (original datafrom GSS. CPI was used to compute real earnings.)_ 9
with potential customers through network programs, as well as offer financial assistance. This program design will be implemented in Malawi through the Financial <u>Inclusion and Entrepreneurship Scaling</u> project of the World Bank.

**Box 1: The context of Malawi: Proliferation of programs with large gaps**

To inform the design of the entrepreneurship support program component of the <u>Financial Inclusion and Entrepreneurship Scaling Project, the World Bank team met with key actors of the entrepreneurial</u> ecosystem in Malawi including (i) policy makers; (ii) several small and medium firms (randomly selected from the World Bank enterprise survey sample frame, 2014; beneficiaries of current support programs); (iii) large firms in various sectors (identified beneficiaries of current support programs) (iv) development partners; and (v) business development service providers. In addition, the World Bank team also conducted a complete mapping of firm support interventions in Malawi. Discussions with the stakeholders and the mapping of existing programs supporting firms clarified that the multiplicity of programs have not been able to make much progress in forming a critical mass of firms that can contribute to a vibrant private sector in Malawi.

The World Bank team identified the following key gaps in current programs: 4
, which hinders the usefulness of quality-based vouchers. Regardless of specific features and locations, what makes vouchers effective is that, by harnessing the power of the market, they trigger a supply expansion and deliver widespread gains, whereas attempts to publicly control supply shrink the market and only benefit those initially using center-based provision.

Although no other study models both supply and demand of child care, numerous contributions have focused on a single side of the market. On the demand side, a large literature looks at parental labor supply, child care use, and child development. The first strand of this literature starts with the early contributions based on static labor supply models with unitary households that make employment and child care decisions.<sup>6</sup> These papers use various US datasets and estimate reduced form and structural models to identify the effect of child care costs on female labor supply and child care demand. They find that, consistent with the neoclassical model, maternal employment and use of formal child care are sensitive to child care costs. Child care subsidies, particularly for the poor, can substantially increase labor force participation and child care use. Our modeling of parents’ labor supply is similar to that of these first-generation papers.

The second strand of the demand-side literature conducts impact evaluation of child care subsidy policies to estimate their effect on maternal employment and child development outcomes.<sup>7</sup> For example, Baker et al. (2008) analyze the introduction of the $5-dollar-a-day

> 6See, for example, Heckman (1974b), Blau and Robins (1988), Ribar (1992), Kimmel and Connelly (2007).

> 7There is a growing literature on child development impacts of parental leave policies (e.g., Dustmann and Schonberg (2012) and Carneiro et al. (2015)).

4