rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

There has been a renewed interest in intergenerational economic mobility over the last few decades, with heightened concerns about widening inequality despite significant growth and poverty reduction in many developed and developing countries (World Development Report (2006), The Economist (2012)). Notwithstanding the recent interest, intergenerational economic persistence in developing countries remains an under-researched area, primarily due to data limitations.<sup>2</sup> A major issue that has stunted progress in this research agenda is that the standard household surveys suffer from truncation, because coresidency is used as a criterion to define household membership (Bardhan (2014), Behrman (1999), Deaton (1997)).<sup>3</sup> A standard household survey such as the Living Standards Measurement Survey (LSMS) done by the World Bank, or the Household Income and Expenditure Survey (HIES) done by national statistical agencies usually includes only the coresident parents and children.<sup>4</sup> Since the pattern of coresidence is not random, most of the studies suffer from potentially serious sample selection bias when estimating intergenerational persistence in economic status. This has discouraged research on intergenerational economic mobility in developing countries.<sup>5</sup> Although potential biases from the coresidency restriction have been a major stumbling block, to the best of our knowledge, there is no evidence on the direction and magnitude of the coresidency bias in the standard measures of intergenerational persistence in developing countries. Are the estimates from the coresident sample biased to such an extent that they

> 2The literature on intergenerational mobility in developed countries is rich with a distinguished pedigree. For excellent surveys of the literature, see Solon (1999), Black and Devereux (2011), Bjorklund and Salvanes (2011), and Corak (2013). A partial list of the contributions includes Bowles (1972), Becker and Tomes (1979), Atkinson et al. (1983), Solon (1992),
EKK also show that firms exporting to more destinations exhibit higher sales in the domestic (French) market. Our data does not include domestic sales, but we can instead look at sales in the most popular destination market for each origin. Let _xil|j_ denote average exports to destination _l_ computed across firms from _i_ that sell in markets _l_ and _j_ and let _l_<sup>_∗_</sup> ( _i_ ) _≡_ arg max _k Nik_ be the most popular destination market for each origin country _i_ (e.g., the United States (U.S.) for Mexico). In Figure 5 we plot log _xilx∗il_ ( _∗i_ )( _|il_ <u>)</u> _∗|j_ ( _i_ )<sup>(vertical axis) against log</sup> _NNil∗ij_ ( _i_ )<sup>(horizontal axis) for</sup> all _i_ and _j_ for the core sample.<sup>16</sup> It is very clear that the results derived by EKK for French firms remains valid for our data with many origin countries: firms that sell in more markets are more productive as proxied by their sales in their origin country’s most popular destination market.

> 15Similar findings are obtained in unreported plots where the destination fixed effects are based on the extended sample and all country pairs or based on the core sample and either country pairs for which _Nij >_ 100 or all country pairs.

> 16The EKK estimating sample includes only firms with sales in France. To implement an approach comparable to theirs, we drop all firms from country _i_ that do not sell to _l_<sup>_∗_</sup> ( _i_ ), so the sample includes only _Nil∗_ ( _i_ ) firms for country _i_ . This implies that all firms that make up _Nij_ are also selling to _l_<sup>_∗_</sup> ( _i_ )
vs. employees in 152 surveys and 42 countries. Over 50% of these surveys are from Europe and Central Asia (33% from high income countries ECA countries, 18% from developing ECA countries) and 34% are from Latin America.

There may also be substantial differences between formal and informal employees, especially in developing countries. We identify formal employees as those who exhibit one or more of the following characteristics: union membership, employer pays for social security or health insurance, or has a formal work contract. Employees who are not identified as formal are classified as informal. We are able to examine differences between formal and informal employees, and between self-employed workers vs. informal or formal employees in 34 countries (190 surveys). The sample of countries where we can identify formal from informal employees is dominated by Latin America; 90% of surveys where we can identify formal and informal employees are from Latin America.

# **IV. Methodology: Estimating the Self-Employment Earnings Penalty/Premium**

We estimate the earnings premium/penalty in each survey using individual worker-level (i) data to estimate the following earnings equation for each country(c)/year(t) combination:

[1]

# Where

- Yict is the dependent variable, self-reported monthly earnings of worker i in country c in year t.

- Xict is a vector of eight variables that partially control for observed differences between workers and industries. These are: years of education, years of education squared, age, age squared, a gender dummy variable, an urban/rural dummy variable, a set of dummy variables for one-digit industry code, and a set of dummy variables representing the frequency of wage payments.<sup>6</sup>

- ���� is the error term

- ����� is a dummy variable indicating whether the worker is self-employed (1) or a wage and salary worker (0)

- EPct is the average self-employment earnings premium, estimated separately for each survey (
# **Table 8. Welfare Outcomes**

||Percent of|Log total<br>|Food<br>|Welf|<br>are standardize|<br>d index|
|---|---|---|---|---|---|---|
||output sold|household<br>income|security<br>PCA index|Full sample|Baseline<br>sample|Matched<br>sample|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Beneficiary|0.249***|-0.594|0.403**|0.288**|0.290|0.314|
||_0.001_|_0.145_|_0.033_|_0.026_|_0.121_|_0.233_|
|Lottery|0.162|0.257|0.318|0.220*|0.458**|0.520**|
||_0.128_|_0.500_|_0.223_|_0.090_|_0.012_|_0.025_|
|Lottery x Beneficiary|-0.058|0.744|-0.281|-0.131|-0.237|-0.319|
||_0.544_|_0.117_|_0.282_|_0.413_|_0.338_|_0.307_|
|R-squared|||0.010|0.018|0.037|0.032|
|Mean of dep. var. for NB in NL vil.|0.125|12.583|-0.758|-0.199|-0.320|-0.340|
|P-val of B = NB in L villages|_0.000_|
such forms of engaging civil society, both to deliver project benefits and to address the root causes, perhaps, of conflict, concludes with strikingly mixed messages (Mansuri and Rao, Forthcoming). While there are some qualitative cases of success in both delivering benefits to the poor and to ex-combatants, and in engaging civil society to confront violent actors, others find that the poor and marginalized find it even harder to access project benefits in conflict areas, and that governments only selectively scale-up such projects in areas where they command political support. The authors of the positive outcomes measured in Liberia, Fearon et al (2011), point out that their results may not speak to addressing conflict _per se_ , because conflict usually occurs at higher levels than that of the local “community” targeted by CDD/CDR projects. It is even possible that strengthening cohesion _within_ communities may worsen conflict _across_ communities.

**_In sum._** Interventions that organize civil society to improve the compact and client-power can be effective; however, much depends on project design and initial conditions of inequality and social cohesion that vary across communities.  This does not imply that community participation in projects involving public goods will always fail in communities with high levels of inequality or weak social cohesion (low “social capital).  Khwaja (Forthcoming) finds that modifying project design can
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# II. **DATA, CONTEXT, AND METHODS**

# **Data and descriptive statistics**

The analysis is based on the China Health and Nutrition Survey (CHNS).<sup>4</sup> The CHNS is a longitudinal survey that covers nine out of China’s 33 province-level divisions.<sup>5</sup> Four counties, stratified by income, were randomly selected in each of the 9 provinces. In addition, the provincial capital and a lower income city were selected when feasible. Within the 36 counties and urban areas, 190 primary sampling units (villages and urban neighbourhoods) were selected randomly. The paper draws on data from four rounds of the CHNS: 1991, 1993, 1997, and 2000.<sup>6</sup> In each year, we focus on the heads of households. The total number of observations for each year is 2,368 (1991), 2,627 (1993), 2,985 (1997), and 2,667 (2000).<sup>7</sup> The four rounds of the CHNS panel cover a period of dramatic change in China. In this paper we focus on how changes in self-assessed health (SAH) relate to changes in economic outcomes. SAH—sometimes referred to as General Health Status—is based on self-evaluation of health status according to a scale of four or five, typically ranging from ‘poor’ to ‘excellent’. SAH is popular in the empirical literature because it has been shown to be highly correlated with subsequent morbidity and mortality.<sup>8</sup> In the parts of China covered by the CHNS, SAH has worsened gradually over the four rounds of survey (Table 1).<sup>9</sup> In 1991, 27% of heads of households rated their health as fair or poor; by 2000, this had increased to 37%. This worsening is also reflected in
the difference declines somewhat at the higher income deciles. The average trip distance differences between men and women are also relatively consistent across travel modes as reported in the ENMODO 2018 household travel survey although are more distinct in the case of travel by car or by public transport than in the case of the non-motorized modes. Similarly, data from a survey implemented by the team in AMBA with current private motorized vehicle users broadly corroborates this pattern: 9.2 percent of women have commutes or other types of “typical” trips that last less than 10 minutes, compared to 5.2 percent of men.

# **Gender norms may play a role in women’s travel decisions in AMBA and, specifically, the distances**

**traveled** . Even if the analyzed data does not explicitly ask questions about intrahousehold resource allocation between women and men, from the ENMODO 2018 household travel survey it can be inferred that gender roles contribute to defining women’s decisions of when are how far to travel. The more family members there are in the household, the farther men seem to travel, possibly to access better economic opportunities, while for women with one family member the distance decreases once they add an additional one.

**Thirdly, public transport appears to serve more women than men, especially in northern AMBA.** Women constitute the majority of public transport users, with the total number of daily public transport trips made by women (4.07 million) in October 2021 being 13 percent higher than those made by men (3.59 million), and public transport represents a greater share in women’s overall mobility compared to men’s

> 2 There are a total of 7.34 million men and 7.75 million women in AMBA. The largest difference in population size by gender is in the older age group (64 and older), with nearly 990,000 women but only about 650,000 men.
Figure 10: Education profile of Part-time employees. (Mid-1990s/Mid-2010s) <!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Argentina Uruguay Brazil Chile Peru Bolivia Dominican Mexico El Salvador<br>Republic<br>Incomplete primary (starting point) Incomplete primary (ending point)<br>Primary (starting point) Primary (ending point)<br>Secondary (starting point) Secondary (ending point)<br>Tertiary (starting point) Tertiary (ending point)<br><!-- End of picture text --> Source: Own calculations based on Household surveys On the other hand, in the case of temporary employment, the improvement of the educational profile is more evident and generalized than in part-time employment (Figure 10). In fact, in all the cases in which temporary employment was identified, taken together, the share of workers at the secondary or higher education increased in proportion in the last two decades. The countries in which the improvements in the education profile are less deep are Chile, where the participation of workers with secondary education decreased in the last two decades, even though this decrease is more than compensated by the greater proportion of tertiary workers, and El Salvador, where the share of workers with tertiary education remains almost constant.

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Global Development, Spetember 2020a.

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from the lower resource-cost of the domestic banking sector.

Interestingly, we observe the above results when we define the foreign bank share is in terms of numbers rather than asset size. The entry of foreign banks obviously precedes their gaining market share. Our results thus suggest that the benefits of foreign bank entry accrue rather early in time, before foreign banks have gained substantial market share.

The remainder of this paper is organized as follows. Section 3 describes the basic approach of the study. Section 3 discusses the data. Section 4 presents the empirical results. Section 5 concludes.

# 2. · _The approach_

Below, we present information from the income statements of domestic and foreign banks. As a measure of bank efficiency, we consider the accounting value of a bank's net interest income over total assets, or _net margin/ta._ To reflect bank profitability, we consider the bank's before-tax profits over total assets, or _before tax profits/ta._ From the bank's income statement, _before tax profits/ta_ satisfies the following accounting identity: 3
0.246|0.523|||
|mortality|||(0.34)|(0.27)<sup>_∗_</sup>|||
|Child dependency|||||-0.162|0.125|
|ratio|||||(0.09)<sup>_∗_</sup>|(0.22)|
|Aged dependency|||||-0.083|0.129|
|ratio|||||(0.10)|(0.12)|
|_F_||2.867<sup>_∗∗∗_</sup>||3.001<sup>_∗∗∗_</sup>||3.292<sup>_∗∗∗_</sup>|
|Hansen _J_<br>||66.120||14.671||60.716|
|AR(2)||0.548||-0.258||0.380|
|Estimator<br>|RE|S-GMM<br>|FE|S-GMM<br>|RE|S-GMM<br>|
|Instruments<br>_N_|608|74<br>608|203|64<br>203|633|82<br>633|

> * Growth rates calculated from GDP data in real 2000 U.S. dollars. Sampling frame selected to maximize sample size, and is hence unbalanced. Data periodicity are fiveyear averages. All variables are in log form. Heteroskedasticity and autocorrelationrobust standard errors are reported in parentheses. A lagged dependent variable (GMM only), period fixed effects, and a constant term (all specifications) were included in the regressions, but not reported. _∗_ indicates significance at 10 percent level,<sup>_∗∗_</sup> indicates significance
effectiveness of export promotion agencies (EPAs) based on a rich survey of EPAs across 88 developed and developing countries. The goals of EPAs are to help exporters understand and find markets for their products and services and can be divided into four categories: (a) country image building (advertising, promotional events, but also advocacy); (b) export support services (exporter training, technical assistance, capacity building, including regulatory compliance, information on trade finance, logistics, customs, packaging, pricing); (c) marketing (trade fairs, exporter and importer missions, follow-up services offered by representatives abroad); and (d) market research and publications (general, sector, and firm-level information, such as market surveys, on-line information on export markets, publications encouraging firms to export, importer and exporter contact databases) (Lederman et al. [2010], pp. 257–258). For 21 of the 73 developing countries surveyed, the authors find that EPAs receive budgetary support from multilateral donors such as the World Bank. The authors estimate the effect of EPAs’ expenditures per capita on overall exports per capita at the country level, accounting for selection bias in survey responses and for potential reverse causality. Their main conclusion is that, on average, EPAs have a significant positive effect on exports. Their estimates also point to the importance of EPAs’ services for overcoming foreign trade barriers and solving asymmetric information problems associated with exports of differentiated goods. In addition, they find evidence of strong diminishing returns, suggesting that small is beautiful as far as EPAs are concerned. However, the authors acknowledge that cross-country regressions cannot fully capture the heterogeneity of policy environments and institutional structures in which EPAs operate; hence, more detailed studies or project-type analyses are needed to provide specific policy advice.

On trade facilitation, Helble, Mann, and Wilson (2009) examine the responsiveness of trade flows to various types of aid for trade — linked to reform of trade policy and regulation, trade development (productive
Figure 6. Gender gap by age group and type of activity (men minus women). Hours per day Panel A. Market work <!-- Start of picture text --> 7.0<br>6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>15-24 25-44 45-64<br>Panel B. Unpaid domestic work<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>-5.0<br>-6.0<br>-7.0<br>15-24 25-44 45-64<br>Albania Argentina Bangladesh Colombia Ecuador Ghana Guatemala Iraq Mexico Moldova Mongolia Pakistan WBG Peru South Africa Timor Turkey Uruguay USA<br>Albania Argentina Bangladesh Colombia Ecuador Ghana Guatemala Iraq Mexico Moldova Mongolia Pakistan WBG Peru South Africa Timor Turkey Uruguay USA<br><!-- End of picture text --> Source: Authors’ elaboration based on survey data.

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We begin by documenting the status quo of the marketplace for professional business services in Lagos and Abuja. We assess brand recognition of service providers amongst applicants to a government firm support program to show that the majority of service providers are unknown by most firms. We then conduct a survey of these providers to determine how easy it is to find providers, and to examine how they gain clients and the service guarantees they provide. We find there is no centralized database of providers, and that providers typically do not offer any sort of financial guarantee on their services, and rely largely on word-of-mouth for gaining new clients. As such, existing use of these services relies more on informal than formal reputation mechanisms, and potential clients have limited information about competing providers in the market. We then send mystery shoppers to visit and rate the quality of a set of accounting, marketing, and human resource specialists, to give us an independent measure of provider quality.

With these pieces, we develop a business services marketplace, consisting of a website that contains information about the different providers operating in a given geographic location, along with their brand logos, taglines describing their services, and contact information. We conduct a randomized experiment with 1,054 firms with 2 to 15 workers in Lagos and Abuja. The control group just receives a baseline interview and follow-up interview six months later. The treatment group is shown the business services marketplace, and randomized into one of four sub-treatments that vary whether they are just shown information, or also quality ratings (taken from the mystery shoppers). Firms are asked to make incentivized rankings over providers on the platform. We find that firms do respond to the quality ratings provided, with the average star rating (out of 5) of their top three-ranked providers increasing by 0.2 to 0.4 stars. Firms that receive the quality ratings treatment are less likely to choose low-quality (3 star or lower) providers and more likely to choose the top-ranked (5 star) providers. Providing quality ratings
algorithm to predict the existence of links among missing nodes using data on the characteristics and linking behavior of non-missing nodes. This approach is challenging to apply in the context of informality, as informal firms are missing in a non-random manner and information on their linkages are not available at a sufficient granularity to recover a network with sectoral and spatial heterogeneity.<sup>10</sup> The paper is organized as follows: Section 2 describes our data. Section 3 examines how representative the trade patterns captured in the administrative data are of overall economic activity and discusses the role of the informal sector. We present and estimate a network formation model in Section 4. Section 5 describes the patterns we observe in our revised network that now incorporates informal firms, while Section 6 analyzes the sensitivity of these results to alternative assumptions.

> 9See L ̈u and Zhou (2011) for a survey of algorithms used for link prediction, a technique commonly applied in network reconstruction.

> 10Rare instances where researchers have used survey data to collect information on firm-to-firm links of informal firms focus on specific sub-sectors. Moreover, explicitly prompting for the tax status of trade partners adds an additional challenge due to the sensitive nature of this information.

6