rafmacalaba/data-use-mentions-extended huggingface dataset:
employees collected three fiscal years before the survey fiscal year, we focus on permanent full-time employees rather than general full-time employees.

The firm growth rate is calculated as the log difference between the current number of employees and the number of employees three fiscal years before the survey fiscal year. The formula for employment growth is as follows: where ��� is firm size, and ���� employment growth, for firm _i_ at time _t_ .<sup>3</sup> The description and summary statistics for the employment growth variable are reported in table 1.

World Bank Enterprise Surveys are conducted to provide information on different aspects of the business environment and the performance of firms. The core questionnaire, which contains survey questions answered by business owners and top managers around the world, provides both subjective and objective information on the business environment that firms confront. The questionnaire includes a section asking firms to rank 15 components of the business environment, indicating which represent the biggest obstacles, and to evaluate these 15 components on a scale of 0–4 (0 being no obstacle, 1 a minor obstacle, 2 a moderate obstacle, 3 a major obstacle, and 4 a very severe obstacle). Summary statistics for the related variables are provided in table 1.

These subjective evaluations show the severity of obstacles across regions and countries. This makes it possible to identify the top obstacles and examine which obstacles firms consider to be the most important. But because the data are subjective—reflecting entrepreneurs’ perceptions of the impact of the business environment on firm operation, with successful entrepreneurs perhaps likely to consider the business environment to be less restrictive—we need to control for firm characteristics in explaining firm growth. In addition, we need to include objective measures of business environment constraints.

The World Bank Enterprise Surveys provide a large set of objective measures of business environment constraints. In addition to subjective information on access to finance as an obstacle, the questionnaire also collects objective information on aspects of financial access, allowing us to create
non-completion, 67 percent of participants refused to respond and one-third reported connectivity problems.

Finally, we present some descriptive statistics of the sensitive information measures (stress and perceptions about effectiveness of physical punishment) in panel B. On average, caregivers in our sample in the midline survey display low levels of mental health—about 43 percent exhibit an above-normal level of stress—and more than half of participants report that physical punishment is an effective method to discipline a child. These levels are very similar in the baseline survey (31 percent report high stress and 48 percent report that physical punishment is effective), but they are significantly lower in the endline survey (only 17 percent of participants report high levels of stress and only 37 percent agree that punishment is an effective method of discipline).

# **4 Empirical Strategy**

To identify whether response rates, composition of respondents, and measures of consistency in the reporting of sensitive issues are affected by survey mode, we exploit the random assignment of each participant to a particular survey method and estimate the following regression: where _Yi_ stands for the response rate, sensitive issue outcome, or consistency of reports measured for individual _i_ . _Phonei_ is a survey mode indicator that takes the value 1 if individual was assigned to CATI or 0 if she was assigned to the WhatsApp group.<sup>�</sup><sup>_n_</sup> _j_<sup>_Xji_is a vector of</sup><sup>_j_time-invariant participant characteris-</sup> tics, which include age and gender. We also add municipality fixed effects ( _Mi_ ) to control for time-invariant shocks at the municipal level and to improve the precision of the estimates. The coefficient associated with the survey mode indicator in Equation (1), _δ_<sup>ˆ</sup> , captures the impact of using the phone-based survey mode and represents our coefficient of interest (ITT). _εi_ is an idiosyncratic error term. Since the randomization occurred at the individual level and
Our paper is related to two literatures. Several papers have estimated the correlates of employment growth in large cross-sections of countries (Crivelli, Furceri, and Toujas-Bernaté 2012; Kapsos 2005). World Bank (2024) summarizes estimates of the elasticity of employment with respect to output growth from this body of literature. Relative to this literature, we have two key contributions. First, a key distinguishing feature of our analysis is that we examine changes in employment ratios, rather than employment growth. This is critical because many EMDEs have rapidly growing working-age populations, and it is difficult to interpret a given level of employment growth without controlling for population growth. Second, our analysis focuses on long run outcomes, whereas the above mentioned papers often focus on short run effects.

A second strand of literature uses the World Bank’s Enterprise Surveys to study firm-level employment, often in specific countries. (See, for example, Aga and Francis 2015; Ayyagari, Demirgüç-Kunt, and Maksimovic 2011; Khan 2023). Relative to this literature, our key contribution is to use these Surveys to link long run aggregate employment outcomes to firm-level constraints. Ours is the first analysis of the extent to which government regulations on labor, land, finance, and trade help or hinder the absorption of a growing working-age population into employment in EMDEs.

The remainder of the paper is organized as follows. Section 2 documents the data and methodology. Section 3 summarizes patterns in steady-state EWAPs and the effect of productivity and population growth. Section 4 shows the correlates of these steady-state EWAPs. Section 5 concludes and discusses the policy implications.

# **2. Methodology and data**

## **2.1 Conceptual framework**

Economy-wide employment can be thought of as the equilibrium outcome of labor supply by households that need to earn income to consume and labor demand by firms that need labor as an input into production. The natural starting point for thinking about dynamics in aggregate productivity and employment is the one-sector growth model. One limitation of this framework
at the times when no-till practices are most likely to occur, the lack of temporal frequency makes it difficult to detect impacts with existing remotely sensed pollution data.

In the current study, we utilize a recently released dataset of highly localized pollution estimates for the United States at a daily scale.<sup>22</sup> These estimates combine data from multiple sources including satellite measures of aerosol optical depth (AOD), emissions inventories, and ground-based pollutant monitors. These data are combined in a multi-model ensemble approach to estimate a pollution surface that provides daily estimates of PM2 _._ 5 on a 1km _×_ 1km grid for the entire U.S.

We estimate the relationship between changes in no-till adoption and pollution levels econometrically with a two-way fixed effects model. Our model examines how daily pollution levels in a county change during the period that farmers are most likely to be engaged in tillage in that county. We define the period when tillage is most likely to occur based on data on crop specific harvest times by state reported to the USDA (for full details see SI1.1).<sup>23</sup> We examine how pollution levels in this “tillage period” change from year to year within a county as the level 3
# **Figure 2: For some developing countries, remittances from other low and middle-income countries are a substantial share of total remittances received**

Remittances by receiving low and middle-income region, 2007 (US$ billion) <!-- Start of picture text --> From high-income countries<br>Bangladesh<br>Brazil<br>From low and middle-<br>China<br>income countries<br>Central Asia<br>India<br>Indonesia<br>Low-Income Sub-Saharan Africa<br>Mexico<br>Middle-Income Middle East and North Africa<br>Other Former Soviet Union and Other Eastern Europe<br>Other Latin America and Caribbean<br>Other Low-Income South and East Asia<br>Other Middle-Income Asia<br>Other Middle-Income Sub-Saharan Africa<br>Pakistan<br>South Africa<br>Turkey<br>0 10 20 30 40 50<br><!-- End of picture text --> Source: Data from Walmsley et al. (2013) 11
<!-- Start of picture text --> (5-year moving average annual GDP growth)<br>14<br>12<br>10<br>8<br>—<br>6 D SIINTI Mh ,<br>c. ee<br>4 ZN. SY<br>0<br>1960-1965 1966-1971 1972-1977 1978-1983 1984-1989 1990-1995 1996-2001 2002-2007 2008-2013 2014-2019 2020-2025<br>=== China —— Developing countries excl China —— High-Income Countries<br><!-- End of picture text -->
for residential properties.<sup>10</sup> Given the relative illiquidity of the real estate market and the lack of transaction data, it is challenging to determine property values. To facilitate extensive margin compliance, property owners are allowed to self-declare their property value, subject to the caveat that the government reserves the right to purchase the property from them at said value. Commercial properties with high revenue potential are valued by a valuation firm

> 9 Although property owners are required to submit a photograph at the time of property registration, these records are typically kept in hard copy and not organized in a way that allows for easy matching with electronic payment records for enforcement purposes.

> 10 Commercial properties are those that are used for income-generating activities, such as a rented apartment, a store, or an office building, whereas residential properties are used strictly for housing and do not generate any income.

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# **III. Analytical Framework**

As noted above, the primary data sources that most commonly underpin distributional analysis in India are the NSS data collected by the National Sample Survey Organization. These are cross-sectional surveys that are regularly fielded over time, offering at best a snapshot of consumption-based welfare outcomes at specific moments of time. In this study we are interested in tracking consumption mobility and to pursue this question we convert the NSS surveys into synthetic panels. We implement an approach that has been recently introduced into the literature (Dang and Lanjouw, 2013; Dang, Lanjouw, Luoto and McKenzie, 2014) and provide a brief description below. We also briefly describe the approach followed to specify a vulnerability line, which allows us to then separate our population into our three categories of poor, vulnerable and secure.

- **III.1. Overview of Synthetic Panel and Vulnerability Analysis Methods** Let xij be a vector of household characteristics observed in survey round _j_ (j= 1 or 2) that

are also observed in the other survey round for household i, i= 1,..., N. These household characteristics include variables that may be collected in only one survey round, but whose values can be inferred for the other round. These variables may be roughly categorized in three

# types

- i) time-invariant variables such as ethnicity, religion, place of birth, or parental education,

- ii) deterministic variables such as age (which, given the value in one survey round, can then be determined given the time interval between the two survey rounds),<sup>8</sup> and

> 8 To reduce spurious changes due to changes in household composition over time, we restrict the estimation samples to household heads age, say 25 to 55 in the first cross section and adjust this age range accordingly in the second cross section. This age range is usually used in traditional pseudo-panel analysis but can vary depending on the cultural and economic factors in each specific setting.
interest.

The official statistics for children at ages 14 and 15 in 1998 shows that 21.6 percent were in the workforce with about half (46 percent) in the formal sector. In 1999, the proportion of 14 and 15-year-olds in the labor force dropped slightly to 20.5 percent, but the proportion in the formal sector almost halved (9.6 percent vs. 5.3 percent). Most of the fall in participation in the formal sector were among children aged 14 (1.76 percent in 1999 compared to 5.8 percent in 1998). These numbers suggest that those who turned 14 before the ban were less likely to be affected by the law change. This is consistent with the monotonicity assumption made in the fuzzy RD design.

> 26 The results are available upon request.

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Policy Research Working Paper 5711

# **Abstract**

In this paper, the authors use a linked employeremployee database from Brazil to examine the impact of trade reform on the wages of workers employed at heterogeneous firms. The analysis of the data at the firmlevel confirms earlier findings of a differential positive effect of trade liberalization on the average wages at exporting firms relative to non-exporting firms. However, this analysis of average firm-level wages is incomplete along several dimensions. First, it cannot fully account for the impact of a change in trade barriers on workforce composition especially in terms of unobservable (timeinvariant) characteristics of workers (innate ability) and any additional productivity that obtains in the context of employment in the specific firm (match specific ability). Furthermore, the firm-level analysis is undertaken under the assumption that the assignment of workers to firms is random. This ignores the sorting of worker into firms and leads to a bias in estimates of the differential impact of trade on workers at exporting firms relative to nonexporting firms. Using detailed information on worker and firm characteristics to control for compositional effects and using firm-worker match specific effects to account for the endogenous mobility of workers, the authors find the differential effect of trade openness on wages in exporting firms relative to domestic firms to be insignificant. Consistent with the models of Helpman, Itskhoki, and Redding (2010) and Davidson, Matusz and Schevchenko (2008), they also find that the workforce composition improves systematically in exporting firms in terms of innate (time invariant) worker ability and in terms the quality of the worker-firm matches.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at Pravin_Krishna@jhu.edu.

_The Policy Research Working Paper Series disseminates the
Number of<br>countries|90|90|90|90|90|90|90|90|90|
|Economic<br>controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Note: Political conflict proxied by battle-related deaths. Bootstrapped standard errors are in parentheses. *** p<0.001, ** p<0.01, * p<0.05, + p<0.10

# **Within-sector heterogeneity**

The analyses so far distinguish between non-resource and resource industries but there might be substantial within-sector heterogeneity that may affect our results. There are substantial differences between the manufacturing and service industries, for example, in terms of sunk costs and labor intensity. There might also be considerable heterogeneity in the natural resource category. Investments in the hydrocarbon industry (oil, gas and coal) might not be sensitive to political violence because they occur in remote locations (offshore). Recognizing these differences, we re-estimate the model using more disaggregated data on manufacturing and services FDI flows and hydrocarbon and nonhydrocarbon FDI flows.

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, etc.<br>Marketing, branding, international presence and promotion efforts<br>Sectoral, professional or other forms of associations (e.g. chambers<br>of commerce) and consultations<br>Development of trade corridors, and other regional forms of hard<br>and soft networks (e.g. regional regulatory agency, regional<br>distribution network, etc.)|•<br>Global competitiveness index – business sophistication: extent of<br>marketing, state of cluster development, value chain breadth,<br>control of international distribution production process<br>sophistication, delegation of authority (WEF GDI 11.05-11.09)<br>– goods market efficiency<br>•<br>Value of e-commerce, number of ICT firms, number of secured<br>servers (WDI, ITU, national statistics)|
|•<br>•|Development of regional markets and stocks / boards of trade,<br>price regulation mechanisms<br>Organization of the value-chains and sectors (filières), including<br>storage and distribution channels|•<br>Post-harvest losses (African Postharvest losses Information System)|
|•|Development of e-commerce (e.g. infrastructure, legal framework,<br>protection of data,securityofpayments,etc.)||

Source: Authors based on OECD, 2012a

# • _Other incentives_

Absence of flexibility in domestic laws, or deficiencies in domestic services and infrastructure, sometimes lead countries to offer an offshore status to foreign firms that are part of GVCs and export all or most of their production. Offshore status includes incentives such as tax cuts, reduced administrative and legal constraints, special customs procedures, etc. In the case of special economic zones (SEZs), some dedicated infrastructure and services (e.g., more reliable access to energy or telecommunications, one-stop shops) add to the attractiveness of the zone (see Farole and Akinci, 2011).
A further contribution is the use of novel geo-localized displacement data collected by the International Organization for Migration (IOM) that allow us to examine the arrival of IDPs in host communities as a channel through which the spatial spillover effects of conflict potentially operate. The literature on the effects of forced displacement on host communities has studied labor market outcomes (Ruiz and Vargas-Silva, 2013; Ruiz, Isabel and Vargas-Silva, Carlos, 2015; Alix-Garcia _et al._ , 2018; Ayenew, 2021; Verme and Schuettler, 2021), rent and food prices, and overall well-being of host-community members (Verme and Schuettler, 2021). A more recent literature seeks to better understand the extent to which displacement accentuates gender vulnerabilities among the IDPs themselves— for example, by looking at risks of gender-based violence and intimate partner violence among displaced people (Kelly _et al._ , 2021a; Ekhator-Mobayode _et al._ , 2021; Kelly _et al._ , 2021b). It also examines the role of gender norms in shaping outcomes such as school enrollment and the prevalence of domestic work among refugees (Krafft _et al._ , 2021), and it analyzes how gender norms themselves may change with displacement (Rubiano Matulevich, 2021). Our paper differs from this literature by elucidating the influence of IDPs on gender outcomes of host-community members.

This paper combines different geo-referenced databases. We use cross-sectional household survey data from the Nigeria Demographic and Health Surveys conducted in 2013 and 2018. We complement this database with information on geo-referenced violent events taken from the Armed Conflict Location and Event Data Project (ACLED), which uses data gathered through text-recognition technology. In addition, we use displacement data from the Displacement Tracking Matrix data project of the IOM. We use this database to calculate the number of IDPs residing within different distance radii from village centroids.

Our empirical strategy exploits the distance from the village centroids to the location of violent attacks in Nigeria occurring during the period 2007 to 2018. Specifically, we count the number of violent events occurring beyond
62)|-0.308<br>(-1.48)|
|Hard peg dummy|-4.34***<br>(-8.60)|0.593*<br>(1.76)|-0.978***<br>(-6.44)|
|Political stability|0.455***|-0.0290**|0.0994***|
|(0 to 100)|(19.36)|(-2.58)|(11.80)|
|n|1,286|1,232|1,128|
|R-squared|0.275|0.083|0.107|
|RMSE|7.75|3.90|3.04|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

19
poor understanding of the choice environment and the trade-offs involved. We pursue this hypothesis further as we examine the revision behavior in stage two of the experiment.

# 3.2 **Stage Two: Undoing Commitment**

Having described some basic features of preferences under commitment, we now examine the relationship between revision behavior in stage two of the experiment, on the one hand, and preferences under commitment and other participant characteristics on the other. Our goal is to evaluate, quantitatively, the importance of time preferences relative to other motives for revision.

# 3.2.1 <u>Qualitative Features of Revision Behavior</u>

Because this is, to our knowledge, a first attempt to study revision behavior in a field experiment, we begin with a simple description of the choices upon revisiting. Recall that stage two of the experiment applies only to those households whose randomly selected choice was an allocation between 61 and 91 days from the baseline interview. The randomization was designed to favor (with two-thirds probability) the later time frame, so we aimed to revisit 722 respondents and present them with a revision opportunity. Of these we were successful in collecting revision choice data from 661 (91.6%).

A first observation about revisions is that they are common. While the experiment made the original choice clear and salient, 65% of participants (432) made some adjustment to their allocation decision.<sup>32</sup> It could be that implicit experimenter demands caused some participants to feel as though some change was expected of them. However, a large majority (87%) made a reallocation involving a shift of at least two tokens, and 64% made a reallocation involving a shift of at least 4 tokens. Figure 4 presents a histogram of changes in the participants’ allocations to sooner (t=61) upon revisiting, excluding those who made no change (who represent 35% of observations), and illustrates the frequency of relatively large revisions.

A second observation
the RCRE surveys), and find a significantly lower

> 6Note that co-residence in rural areas of the four RCRE provinces was also somewhat higher than what we observe for rural areas of the CHNS panel.

> 7Of course, two very different conclusions are consistent with evidence of greater incidence of co-residence with age in a simple cross-section: the oldest, who are more likely to be infirm, tend to move in with adult children; alternatively, if co-residence does have an impact on the quality of care provided, then perhaps only the elderly living with adult children reach old age.

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