rafmacalaba/data-use-mentions-extended huggingface dataset:
Abbey et al. 2017, Artuc et al. 2019). Exporters across different countries and industries employ more labor, are more productive, skill- and capital-intensive, and pay higher wages than nonexporting counterparts (Bernard et al. 2007, Brambilla et al. 2015, Dutz et al. 2011, Duda-Nyczak and Viegelahn 2018).

Across countries, however, mixed results emerge. Krauss (2015) analyzes trade effects on job growth in 12 East Asian and Pacific (EAP) countries individually.<sup>3</sup> He finds that exporting positively and significantly correlates with firm expansion, especially in larger economies (China, Indonesia, Mongolia, the Philippines, and Vietnam). In contrast, exports do not have much relationship with job growth in small and island states (Fiji, the Lao People’s Democratic Republic, Samoa, Timor-Leste, and Tonga). Dessing (2002) fails to support the optimistic view of trade with respect to employment creation. Examining data between 1980 and 1998 from 18 developing countries plus newly industrialized Republic of Korea to assess the employment effects of trade, Dessing shows that estimated export-employment elasticities are never significantly different from zero. Egypt is another country that failed to experience job growth and poverty reductions despite significant growth in trade (Robertson et al. 2021).

The results from Egypt seem to suggest that it is one of the interesting exceptions to the positive export-employment link found in most countries. Although rising exports usually increase employment through increased labor demand, there are at least four reasons why it might not even when labor supply is constant. First, exporting firms may be too small of a segment in the local labor market to significantly affect aggregate employment. Second, in the presence of hiring and firing costs, firms may hold on to workers, creating a relatively idle “reserve” group of workers to

> 3 China, Fiji, Indonesia, Lao PDR, Micronesia, Mongolia, the Philippines, Samoa, Timor-Leste, Tonga, Vanuatu, and Vietnam.

4
, the main source of pollution in these urban areas is road traffic rather than economic activity, something that we explore further and confirm again in Section 6. This hourly pattern is robust: we reach the same conclusion if we use the stationary or mobile readings, and if we use the average or the median readings.

# **4.3 Firm Survey**

The second data source is a novel survey of manufacturing firms that we conducted in Uganda. The survey took place in all our target 52 sub-counties between September 2018 and July 2019 and was implemented by our partner NGO, BRAC Uganda, in partnership with the Ministry of Trade. Within each target sub-county, urban and semi-urban parishes were surveyed. The survey is described in detail in Bassi et al. (2021). Here we summarize again the key elements of the sampling of firms and survey design, and then focus on those aspects that are particularly relevant for this paper.

> 18In those sub-counties where the stationary and mobile monitors overlap, all active mobile monitors were within proximity of a stationary monitor. The median distance between a mobile pollution measurement and the closest stationary monitor is 2.345 km and 95% of measurements are within less than 7 km from a stationary monitor.

> 19 Of course, we expect the correlation of the pollution readings from stationary and mobile monitors within sub-counties to be less than one as the mobile monitors were potentially hundreds of meters away from the stationary monitors at times.

13
includes a plan to encourage 30 million workers to transfer to the private sector-a figure that represents 30 percent of SOEs workers and 15 percent of the urban labor force. In parallel, the government has initiated reforns to shift the delivery of key services previously provided by SOEs such as housing, health care, education, old age security to new providers in the private sector, public enterprises and local governments.<sup>4</sup> The major focus of this paper is to estimate the likely costs and benefits of SOEs reforms on labor, although we also look at the likely gains attached to a re-allocation of capital between the state and private sectors. The focus is on industry, for whom reasonable historical series of provincial output has been released recently (in the China Statistical Yearbook, 1994-97). In the first part of the paper, we compare the evolution over time of the efficiency in the use of labor and capital in the state and non-state sector and assess the factors behind the change in the relative performanceI of these sectors. In the second part, we simulate the effects of transferring labor from the least efficient sector to the most efficient, and estimate the potential gains in output and the effect on the fiscal accounts and the state of the banking system. In the concluding section, we contrast our previous results with estimates of what can be achieved by transferring capital to the private sector, and offer some suggestions to explain the lack of current focus on the allocation of capital.

> 4 The government also has plans to divest small- and medium-size SOEs, but it has refused to relinquish control over the large "strategic" industries.

2
# **_D. Integration with non-traditional data sources_**

As for the opportunities offered by the integration with non-traditional data sources, also known as ‘digital trace’ data (Kirchberger, 2022), such as big data and citizen-generated data, our assessment is that there are both pros and cons, and that while, at the moment, improving interoperability of household surveys with non-conventional data presents complex issues, this is likely to be the way to go in a notso-far future. On the one hand, big data, such as mobile phone data or smartphone app data, have indeed the potential to complement traditional data and address significant spatial and temporal gaps. As emphasized by the International Organization for Migration (IOM, 2018), the main strengths of these new data sources lie especially in their wide and continuous coverage, flexibility, timeliness, frequency, high spatial, relatively low cost, and their potential to track temporary, circular, and seasonal patterns of migration, which are difficult to capture through traditional sources. Additionally, big data can be a promising tool for better ex-ante sample design, such as first-stage sample selection, and allow one to compute new covariates that would otherwise be costly or difficult to collect at scale, such as labor market referrals, social networks, or mobility and social contact (Kirchberger, 2022). On the other hand, the use of big data comes with significant challenges, among which: i) ownership by the private sector; ii) privacy, data protection and ethical issues in using data automatically generated by users, and human rights concerns; iii) their volume, complexity and “noisiness”; iv) the fact that big data reflect behavioral patterns which may not be representative of the population (selection bias); v) reliability of the selfreported information on social media; difficulties in applying statistical definitions of migration.

<mark>Using the case study of a data science challenge involving West African mobile phone data, Taylor (2016) argues that big data carries with
Figure 2: PISA-based management index by quartile x PISA student outcomes <!-- Start of picture text --> Math Reading Science<br>5.32 5.54 5.32<br>0.91 1.23 0.77<br>0.01<br>-0.25 -0.10<br>-5.91 -5.92<br>-6.71<br>Bottom quartile 2nd quartile 3rd quartile Top quartile<br>PISA-based management score (quartiles)<br>10<br>5<br>0<br>PISA score<br>-5<br>(deviations from the mean)<br>-10<br><!-- End of picture text --> Note: Number of observations: 15,196 schools from 65 countries available in PISA 2012 data. Student outcomes are estimated using five plausible values and collapsed at the school level using PISA’s senate weights. Quartiles of management are built at the country level. Test scores are presented as deviations from the global mean.

34
and stimulate agricultural production in marginal areas, the traditional sector (handicrafts and souvenirs) and transport services.

Assessing the magnitude of the economic impacts of tourism development is not straightforward because of a wide range of activities associated with growth in this sector and the difficulty in defining tourism as an industry. Wide discrepancies across regions and between urban and rural areas, common in developing countries and especially pronounced in Panama, are likely to translate into similarly large variation in the magnitude of the impacts of tourism on growth and poverty by region. This paper estimates the economic impact of the growing tourism sector in Panama at the regional level and assesses the distributional and poverty impacts of the sector’s growth.

# **2. The Role of the Tourism Sector in Panama within the Broader Social and Economic Context**

Panama is attracting tourism not only because of its scenic vistas and vast biodiversity, but also because of its rich cultural heritage. In addition to attracting thousands of business travelers, it is a growing center of coastal, cultural, health, and ecotourism. Surveys of foreign tourists reveal that business and shopping are still the main purposes of the trip for about a quarter to a third of all visitors; recreation is the main purpose for over a third of all visitors, and family visits for around 20 percent. Among activities, shopping, entertainment and business needs are still the main motives for the trip. But adventure, coastal and ecotourism are gaining prominence.<sup>4</sup> One survey reveals that over 40 percent of foreign visitors have spent time at the beach, and 10 percent have participated in ecotourism, among other activities.<sup>5</sup> Another survey suggests that ecotourism is one of the main purposes of the trip for about 15 percent of foreign visitors, while cultural tourism is still in a nascent stage (Table 1).

**Table 1. Activities realized during the trip.**

|_Forei_|_gn tourists_|_Domestic to_|_urists_|
|
<!-- Start of picture text --> Figure 2<br>Evolution of occupational structure, EU-15<br>1995 2000 2005 2010 2013<br>Occupation by task intensity<br>Routine (ISCO 88) Routine (ISCO 08)<br>Non-Routine Manual (ISCO 88) Non-Routine Manual (ISCO 08)<br>Non-Routine Cognitive (ISCO 88) Non-Routine Cognitive (ISCO 08)<br><!-- End of picture text --> Note: this figure shows the evolution of the occupational structure of wage employment of the aggregated EU‐15 countries (excluding Finland, Germany and Sweden) from 1995 to 2013. Occupations are classified according to their task intensity – See Appendix 2 for a detailed description of the classification. The red line indicates the share of wage employment in occupations intensive in routine tasks; the blue line indicates the share of wage employment in occupations intensive in non‐routine, manual tasks and the green line indicates the share of wage employment in occupations intensive in non‐routine, cognitive tasks. The source is the annual EU Labor Force Survey. Because of a change in the classification of occupations used by the survey (From ISCO 88 to ISCO 08) there is a break in the data after 2011.

Our decomposition method follows Bourguignon and Ferreira (2005). We first estimate an occupational choice model linking individual characteristics to occupations, and a set of Mincer equations for each type of occupation and worker, for the initial and final year of a period running from the early 1990s to 2013. We then carry out a set of counterfactual simulations, whereby we alternatively switch parameters and variables’ values in the final year with those of the initial year to assess the importance of each factor. Each counterfactual creates a simulated distribution of wages, which is “in between” the initial and the final distribution. In this way, the full change between the initial and final distribution, in terms of inequality or other indicators, can be decomposed
job quality, considering common factors such as social benefits, wages, and contracts, as well as additional factors like overqualification and child labor. Thirdly, it presents an easily replicable and evaluative approach for assessing labor conditions across different contexts. Finally, evaluating how different economic shocks, such as the COVID-19 pandemic, affect labor conditions is crucial, particularly for informal sector workers and those engaged in face-to-face (F2F) interactions.

This paper is organized as follows: Section 2 reviews the relevant literature in this research domain. Section 3 provides a concise overview of the labor regulation landscape, while Section 4 outlines the methodology employed for constructing the index. In Section 5 we present the diverse findings of the constructed Job Quality Index (JQI), and finally, Section 6 concludes the study with key insights and conclusions.

3
To deal with those problems, we adopt three different strategies. First, we clear out the effect of – one source of – on our productivity growth presumed endogeneity prices by regressing transport cost series on industry multi-factor productivity indices (from the klems database), as well as industry and year fixed effects. We then use the residual from that regression as a proxy for the transportation cost series. By definition, that residual is orthogonal to any productivity-driven price changes that could stem from the changing geographic concentration of industries. This strategy does not deal directly with the transportation price index.

Second, we use us manufacturing industry price indices as external instruments for the transport cost series. This instrumentation strategy is similar to that of Ellison _et al._ (2010), who instrument the us input-output tables and the us industry labor requirements with those of the uk. The underlying idea is the following. Assume that the geographic concentration of an industry increases over time because of unobserved factors that we cannot control for in our analysis. This increasing geographic concentration then raises ad valorem transport costs via price decreases of the industry’s output. Provided that the changes for the us are not driven by the same unobserved factors that affect the spatial concentration of the industry in Canada, but that the us price series **P**<sup>US</sup> trans, _t_<sup>/</sup><sup>**P**US</sup> _i_ , _t_<sup>arecorrelatedwiththechangesin</sup><sup>**P**trans,</sup><sup>_t_/</sup><sup>**P**</sup><sup>_i_,</sup><sup>_t_,theywill</sup> provide valid instruments for the Canadian transport cost series. Two potential limitations of these instruments are the following: (i) there may be common underlying unobserved factors that drive changes in the concentration
The literature on non-fundamental contagion also has an empirical branch. Kaminsky and Schmukler **(1999)** find that spillover effects unrelated to market fundamentals are quite common, and spread quickly across countries within a region. Valdes **(1998)** examines the degree to which comovement of Brady-bond prices is unexplained **by** fundamentals. Interestingly, contagion in his paper is symmetric, applying both on the downside during crises and on the upside during periods of rapid capital inflow. **A** different line of empirical work on non-fundamental contagion examines whether crises are spread **by** particular investor groups. For example, Choe, Kho, and Stulz **(1998)** use transaction data in the Korean equity market to examine whether foreign investors destabilize prices. They find evidence of herding **by** foreign investors before Korea's economic crisis in late **1997,** but these effects disappear during the peak of the crisis, and there is no evidence of destabilization. Since their data include only transactions on the Korean Stock Exchange, these authors cannot examine the transmission of crisis across countries.

# **11.4. Measuring Contagion Trading**

Our approach to testing for contagion is different from the literature reviewed above. Data on individual portfolios allow us to address contagion in a new way-from the trading-strategy perspective. We will use the term contagion trading to mean the systematic selling (buying) of stocks in one country when the stock market falls (rises) in another." To do this we introduce a new measure-a contagion trading measure. Our contagion trading measure is based on the methodology outlined above for measuring momentum trading. Like the momentum measures, we present contagion trading measures at three different levels: whole-fund contagion trading **(C),** manager-only contagion trading

> 11Notice that this definition does not take account of the fundamental-versus-non-fundamental distinction introduced above. The appendix introduces a regression-based approach that allows us to test for contagion with
**Figure 6. Distributional incidence of subsidies (incidence by deciles) in two Asian countries**

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail. _Note:_ All figures are calculated using sample weights. Total expenditure is total household expenditure in all categories. The distribution of expenditure refers to the countrywide distribution of expenditure per capita, i.e., households are ranked according to their expenditure per capita.

**Figure 7. Distributional incidence of subsidy beneficiaries (incidence by deciles) in two Asian countries**

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail. _Note:_ Subsidy beneficiaries are households. All figures are calculated using sample weights. Total expenditure is total household expenditure in all categories. The distribution of expenditure (Jamaica) or income (El Salvador and Panama) refers to the countrywide distribution of expenditure/income per capita, i.e., households are ranked according to their expenditure/income per capita.

# **3.2 How well are existing subsidies targeting the poor?**

Table 3 and figure 8 show the targeting performance of water consumption subsidies in the 10 countries under analysis. Figure 8 shows how access factors ( _SA*C_ ) and subsidy design factors ( _T*R_ ) drive the omega (Ω) value across countries. Table 3 presents the values of each factor for each country. A value below 1 indicates that the factor lowers performance, whereas a value above 1 indicates that the factor boosts performance.

As shown in figure 8, all African, Latin American, and Asian countries present Ω values below 1, demonstrating that water consumption subsidies are poorly targeted to the most vulnerable populations. Access factors (blue dots) are the driving force explaining low Ω values in most 16
_Data processing_ How survey organizations process the data obtained from respondents is also an important issue for measuring the top tail of the income distribution. The main concern is top-coding, where the survey organization does not provide the actual income for individuals close to the top of the distribution but provides the value of some percentile for all respondents above that value- usually as a way of mitigating privacy concerns. The extent of top coding can be large- Burkhauser et al. (2012) document that 4.6% of individuals in the US CPS live in households in which some source of income was top coded.

In LMICs where statistical capacity may not be as good as in rich countries there are other data processing concerns. McLennan et al. (2021) show that in the Tanzanian Household Budget Survey (HBS) public data there are very large labor income values generated from individuals reporting high hourly rates of earnings, which are then multiplied by 8 (hours in a day) and then by 22 days in a month. These errors are so large that the implied tax revenue is substantially higher than actual tax revenue collected. McLennan et al. (2021) highlight that the public release documentation for the Tanzanian HBS contains no information about how such cases are treated. How these were processed and whether enough data is released publicly to allow alternative assumptions will matter when measuring the top tail of the distribution.

Some of the very large hourly earners in Tanzania might be contaminated incorrect outliers but it is possible they could be genuine high earners. The solution used by McLennan et al. (2021) is to cap large values at the 99<sup>th</sup> percentile of the labor income distribution, which is not suitable when trying to measure the top tail of the distribution.

A lack of documentation on this and other edits and decisions taken when processing data from household surveys is likely to be true in other LMICs. Kerr and Wittenberg (2021) note that earnings are imputed in South Africa’s Quarterly
where i is an outcome for household in group at survey wave _t_ , and i is a dummy equal to 1 if household gg _i_ in group is offered public works. All regressions control for group effects gg yy ii gg TT ( ), strata fixed effects ( ), with an ANCOVA specification, and controlling for a vector of gg baseline (pre-determined) covariates ( ).<sup>15</sup> The impact of the public works offer is gg ss λλ λλ captured by _β_ . We estimate this equation separately at two points in time: during the public ii0 XX works program (M, midline) and after the program ended (E, endline).

In order to improve precision of the estimates and to account for random imbalance on observable characteristics, our regressions include the following set of baseline regressors: age of female beneficiary and her husband (if present), number of household members, number of children aged 0-5 years, number of children aged 6-15 years, an indicator for whether the woman is working, share of household members who are inactive, and whether the household is among the poorest 25<sup>th</sup> percentile of the food per capita expenditure distribution. We use this equation to estimate the effects presented in Tables 4 to 9. We also included survey-time indicators.<sup>16</sup>

# _Heterogeneity Analysis_

Our standard attempts to find heterogeneous effects are limited by the parametric modeling assumptions and by one-at-a-time testing for heterogeneity using interaction terms with adjustments for multiple hypotheses testing. Given the large array of potential sample splits for detecting heterogeneity, choosing ad hoc subgroups ex-post creates the possibility of overfitting. To this end, we aimed to minimize the concerns of specification searching by applying a machine learning approach to guide the selection of the relevant dimension of heterogeneity. We follow Chernozhukov et al.’s (2018) general framework, which provides valid inference to test whether there is heterogeneity in impact based on a set of baseline covariates. If heterogeneity