rafmacalaba/data-use-mentions-extended huggingface dataset:
--> _Notes:_ The sample includes 33,336 firms in 47 countries in panel a, 17,949 firms in 33 countries in panel b; 10,939 firms in 10 countries in panel c and 13,794 firms in 13 countries in panel d.

# **5. The Impact of COVID-19 on Globally Engaged Firms: Some Stylized Facts**

## **5.1 Trade Outcomes**

Analysis based on the customs data and the BPS data with the sub-sample of globally engaged firms only brings forth two main findings. _First_ , the pandemic led to a strong decline in quarterly firm-level exports and imports in the first six months of 2020 but the negative impacts dramatically weakened in the rest of 2020, indicating modest adaptation of globally engaged firms, especially exporters, to the pandemic (Bricongne et al., 2012; de Lucio et al., 2020; Pimenta et al., 2021). Firm exports and imports in customs data declined by 41% and 29% in the second quarter (Q2 2020) relative to that of 2019 (Figure 2, panel a). These trade outcomes had already declined in the very early stages of the pandemic (Q1 2020) that began to adversely affect China, a key global destination and source country for trade. Firms were also affected by the pandemic through a substantial decline in their number of products and partner countries in the second quarter of 2020 (Appendix Figure C5, panels a and b). These negative impacts dampen during the rest of 2020 (Q3 and Q4 2020) relative to corresponding values in 2019. Consistent with the analyses of the customs data, the BPS data also confirms a sharp decline in firm exports (as a share of sales) and imports (as share of 12
In addition to the potential for real-time monitoring, the granularity and uniqueness of data captured by online job portals offer unique advantages over traditional survey instruments. Online job advertisements can provide highly granular data on labor market characteristics and demand dynamics at a specific time or location, or for a specific type of occupation. For example, while official statistics collect data on the current level and distribution of wages, online job-portal data focus solely on wage offers, which can help identify emerging trends in the demand for specific skills, the seasonality of hiring practices and firms’ production expectations for specific jobs. The data allow for both a dynamic and static evaluation of the labor market, as well as an examination of specific market segments. Information on the historical growth and spatial distribution of job openings, wage trends and patterns, and competition among job seekers can help policy makers understand how the labor market is evolving in different places and over time.

However, there are important caveats regarding the representativeness of online job-portal data. In order to ensure that findings are interpreted accurately, it is crucial to specify exactly what the data are representing and to clearly define their applicability to the broader labor market. Limitations on representativeness can be addressed by statistically weighting the data according to the industry structure derived from labor force surveys, focusing on segments of the labor market in which coverage bias is less of a problem, and using diverse data sources to conduct parallel analyses and confirm the robustness of findings (Kureková, Beblavý, and Thum-Thysen, 2015).

# **_Application of the Analysis to Babajob Data_**

As noted above, online job-portal data can facilitate an in-depth analysis of specific social groups and segments of the labor market. In this study, Babajob data was used to analyze gender differences in wage offers for similar positions. Women constitute 26 percent of the Indian workforce, and gender disparities in workforce participation rates are an important development issue (United Nations, 2012). An analysis of wage offers for 858,000 jobs posted to Babajob by
are coordinated and decided jointly, inefficient levels of investment may arise. Furthermore, information problems and uncertainty in this investment in human capital may lead firms to invest small amounts in training even though the ex post average return is high. Even though under our current set up we cannot distinguish how much of the variability in returns across firms is due to heterogeneity and how much is due to uncertainty we find an enormous dispersion in the ex post returns to training which may be suggestive of the importance of uncertainty. For example, in our base specification the 5th percentile of the distribution of internal rates of return is -16% and the 95% percentile is 66%. Finally, it is possible that firms would like to invest more in their workers but they are unable to do so because they are constrained (e.g., credit constrained). In that case, investments in training are likely to be suboptimal. Unfortunately we cannot verify empirically the importance of each of these different hypotheses.

The paper proceeds as follows. Section 2 describes the data we use. In section 3, we present our basic framework for estimating the production function and the cost function. In section 4 we present our empirical estimates of the costs and benefits of training and compute the marginal internal rate of return for investments in training. Section 5 concludes.

# 2. Data

We use data from an annual survey collected by the Portuguese Ministry of Employment (Balanco Social) covering all the firms with more than 100 employees operating in Portugal. The survey is mandatory and collects information on hours of training provided by the Pischke (1998, 1999) for an analysis of the consequences of imperfect labor markets for firm provision of general training.

6
bottom panel displays, separately, the real value of gifts from the bride’s family and from the groom’s family.<sup>15</sup> Ideally, to examine how dowry expectations impact household behavior, we would like selfreported data on how much dowry a family expects to give or receive when their child gets married. Unfortunately, we are unaware of any dataset that has this information. Consequently, we construct a proxy for the true expectations—our _expected dowry_ variable—by assuming that, after a child is born, parents form expectations about the dowry they will pay or receive for their child in the

> 11In addition to the IHDS, other researchers have used dowry data from the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) and the Status of Women and Fertility (SWAF) surveys. While the ICRISAT data contain retrospective information on marriages, it is only a small survey of 240 households from six villages in three districts of rural South Central India collected in 1983. Although the SWAF survey is relatively new and was conducted in 1993-94, a key shortcoming of it is that it does not report specific dowry amounts and instead provides five ordinal categories that nominal dowries fall into.

> 12The surveys were administered to household heads who provided information on marriages of other household members. Male heads were asked about their non co-resident children, siblings, and non coresident parents, while female heads were asked about their non co-resident children, and the siblings and non co-resident parents of their husband. One omission that 2006 REDS made is not to collect data on dowries received by co-resident sons of the head. However, since a co-resident son would be married to another head’s non-co-resident daughter (typically from the same state and caste given the structure of the Indian marriage market), the dowry information for co-resident sons’ marriages may still be in our data.

> 13See Chiplunkar and Weaver (2017) for documentation of the prevalence and evolution of dowry in India using the 1999 round of REDS
# **Table A4.1 Captivate Index**

|**Question**|**Factor**<br>**Loadings**|
|---|---|
|A. I feel it the way I teach Science changes a lot.|0.67|
|B. I like and/or enjoy more teaching Science than in previous years.|0.80|
|C. I feel that by implementing the ideas of this training, my students learned<br>more in comparison with other groups and/or subjects.|0.80|
|D. I feel my students develop more skills than in previous years.|0.78|

Author’s analysis based on experimental data generated for this study **.** 14
terms of the private-credit-to-GDP ratio, some countries from the CEE region had reached levels typical of some euro area countries.

**Chart 5. Private credit ratios in selected EU Chart 6. Financial and banking sector assets countries (as % of GDP; 2007 Q4)**

<!-- Start of picture text --> Romania<br>Poland<br>Slovak Republic<br>Czech Republic<br>Lithuania CEE countries<br>Hungary<br>Bulgaria<br>Slovenia<br>Latvia<br>Estonia<br>Finland<br>Belgium<br>Greece<br>Netherlands<br>Ireland<br>Cyprus<br>0% 50% 100% 150% 200% 250%<br><!-- End of picture text --> Source: IMF IFS, authors' calculations <!-- Start of picture text --> 180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>2005 2006 2007 2008 2009 2010<br>Financial sector assets (as% of GDP)<br>Banking sector assets (as % of GDP)<br>Share of banking sector in financial sector (%)<br><!-- End of picture text --> Source: CNB The Czech financial system is bank-based, similar to those in other European countries. While the depth of financial intermediation measured by the total assets of the financial sector in percent of GDP steadily increases, reaching 156 percent in 2010, the share of banks in the financial sector remains stable at around 75 percent (Chart 6). Compared to the euro area, where the financial sector assets account for some 570 percent of GDP, financial sector assets in the Czech Republic are still relatively low. The stock market is shallow in the Czech Republic, with capitalization of around
planning as also found for the main results on cumulative births. Again, this evidence points to the direct role of family planning access on age differentiated fertility patterns as opposed to indirect effects of health services.

# **5 Conclusion**

Despite substantial interest among policy makers in family planning programs, especially in high fertility, low-income settings, the debate about their effectiveness has so far been lacking in reliable empirical evidence. This is partly due to the methodological challenges involved in controlling for the potential nonrandom program placement of family planning programs. Although experimental data provide a theoretically “clean” way to address these concerns, their application to fertility is complex given the time span over which fertility decisions are made and therefore, in practice, only survey data are usually available.

This paper studies the effects of family planning on fertility in Ethiopia and to address potential nonrandom program placement uses a set of novel instruments: the rankings of area characteristics (as opposed to the levels). We argue that such rankings of area characteristics are likely reflective of policy makers’ actual decision process when allocating family planning programs, while not affecting fertility directly. They are intuitive and easy to generate from readily available secondary data like a census or even from the primary data set itself, enabling easy replication of the methodology in other settings.

The results suggest that access to family planning reduces the total number of children born for Ethiopian women without education. The reduction in completed fertility is large at more than one child. This is more than twice the effect reported in other studies, typically from more developed settings. Moreover, the actual impact is likely larger as the results are arguably underestimates. As detailed in this paper, our approach is conservative in attributing no access to women who have some access later in the study period. This biases our impact estimates downward. No effect of access to family planning was found among women with some education, suggesting that family planning may act as a substitute for education in reducing fertility.

There are two likely explanations
produce the iPad and iPhone and why an Indian manufacturer such as Asian Paints, belonging to the chemical and chemical products industry, offers interior design and painting services. _Second_ , because of externalities in consumption, i.e. because consumers prefer to buy a manufacturing product and related services from the same firm, and a firm’s bundling the two enhances the appeal of its products.<sup>3</sup> Thus, Apple also offers financial services and personal and recreation services (e.g. iTunes), which are not necessarily an extension of the production capabilities of the firm, but are convenient for consumers to purchase from Apple.<sup>4</sup> In certain cases, servitization happens for both reasons. For example, Apple offers repair and maintenance services for the iPad and iPhone, because it is both well-equipped to and trusted by consumers to do so. Likewise, Bombay Dyeing, an Indian textile firm founded in 1879, offers retail services to its consumers.

Previous empirical work has examined some implications of both rationales for servitization. Breinlich et al. (2014) propose a model where firms allocate their finite stock of accumulated industry expertise in order to augment the productivity of their goods and services production. Using firm-level data from the manufacturing sector in the United Kingdom, they show that accumulated expertise, as embodied by a firm’s stock of research and development, facilitates the transition to more intensive services provision in the face of goods market trade liberalization. In contrast, a larger physical capital stock is associated with a significantly weaker shift into services. Thus, in the case of a shared finite production asset, enhanced goods market competition, whether foreign or domestic, could motivate firms to shift into services. In these circumstances, increasing services revenue is likely to be associated with declining manufacturing revenue.

> 1 The literature on servitization encompasses both the use of services as inputs and their sale as final products by manufacturers (Lodefolk, 2013). In this paper,
describe these literatures, compare some measures of port efficiency, and discuss the links between port efficiencies and transportation costs.

# **4.4.1 Production Function Measures and Cost Analysis**

This operations research study approach is based on the production function estimation and involves several linear-programming applications. It focuses on (i) identifying an _efficiency frontier_ based on observed inputs and outputs; and then (ii) calculating the distance between a productive-unit (read: port) efficiency and the efficiency frontier. A popular linear-programming application includes the Data Envelopment Analysis (DEA), a non-parametric and deterministic approach sensitive to variable selection and measurement errors. The competing production function estimation approach is represented by the Stochastic Frontier Analysis (SFA)—a parametric and stochastic approach that requires careful decisions over the functional form of the production function.

Blonigen and Wilson (2008) provide an informative literature review of this approach. The main advantage of estimating the port efficiency as the production function is in pinpointing the marginal capacity gained from investing in any particular part of the port infrastructure. This analysis then allows identifying the bottlenecks for any given port and performing an informed cost-benefit analysis when evaluating various investments in ports. However, this type of analysis tends to omit the demand side and thus by itself is not providing a direct link with the transportation costs associated with utilizing the ports.

Recent work by Herrera Dappe and Suárez-Alemán (2016) linked this approach to transportation cost.<sup>36</sup> This estimated individual the DEA for paper first ports efficiency using methodology each year<sup>37</sup> and then merged the average DEA-methodology annual port efficiencies with the OECD Maritime Transport Cost dataset and UN Commtrade US exports from 2000-2007. Next, building on

> 36This paper leverages the data on nearly fifty (50) ports across East Asia, the Middle East, Southeast Asia, and Southern/Eastern Africa. Herrera Dappe et al. (2017) further focused on countries and ports around the Indian Ocean.

> 37It also
was one of the earliest beneficiaries of the Green Revolution with the introduction of modern varieties of wheat and rice in the 1960s. However, recent studies have questioned the sustainability of current intensification strategies (Byerlee and Siddiq, 1994). In light of recent record imports of wheat, the staple food, the issue is clearly critical to agricultural development strategies and food security in Pakistan, as well as in neighboring areas of India.

The paper is developed as follows. The next two sections outline the analytical approach and the major data sources. This is followed by a description of major trends in the agricultural sector, especially those related to technical change and resource quality. Estimates of TFP growth are then presented and productivity growth decomposed into the effects of technology, resource degradation, human resources, and infrastructure through estimation of a cost function. The last section summarizes the main policy implications.

# **Analytical Approach**

Following Lynam and Herdt, a non-positive trend in TFP has been widely accepted, at least by economists, as an indicator of unsustainable resource management (e.g., Cassman and Pingali, 1995; Ehui and Spencer, 1993). However, many practical problems are encountered in choosing an acceptable measure of TFP apart from the fact that positive TFP growth by itself does not

> 2 Huang and Rozelle (1995) estimated the effects of technological change and environmental degradation using secondary statistics, such as area of land eroded. Preliminary work by Lindert (1996) has used soil testing data although he does not decompose the effects of technological change and resource degradation.

2
<!-- Start of picture text --> Figure 2a: City governance and globalization<br><!-- End of picture text --> <!-- Start of picture text --> Good Governance<br>7<br>Low Illegal Party Financing<br>Low Diversion of Public Funds<br>Effectiveness/Trust of Postal System<br>4<br>Poor<br>1<br>Low Globalization Medium globalization High globalization<br>Source: Executive Opinion Survey (2003).<br>Figure 2b: City governance and urbanization<br>City Governance<br>7<br>Good<br>Low Health Access Gap<br>Low Bribery in Taxes<br>Low bribery in Utilities<br>4<br>Poor<br>1<br>Low Urbanization Medium Urbanization High Urbanization<br>Source: Executive Opinion Survey (2003).<br>Figure 2c: City governance and city characteristics<br>7 Low Bribery to affect laws, regulations<br>Good<br>Low Diversion of Public Funds<br>Low Organized Crime<br>City/country City Population Capital City<br>Size Ratio<br>4<br>Poor<br>1<br>Low ratio High ratio Low High Not the Capital<br>Population Population Capital City<br>Source: Executive Opinion Survey (2003).<br><!-- End of picture text --> 28
compared to about 1.5 billion ha already under cultivation (Deininger</mark> _<mark>et al.</mark>_ <mark>2011a). Most of this land is in Sub-Saharan Africa, Latin America, and Eastern Europe (201, 123, and 52 million ha, respectively) although in the latter, relatively higher shares of land are in proximity to markets. As land availability is a key driver of the ―land rush‖, we expect this variable to be positive and highly significant.</mark> Third, while the link between foreign investment and governance has long been highlighted, special attention to land governance may be warranted.<sup>13</sup> To address this issue, we draw on three complementary indicators for general and land governance. First, we use the _Doing Business_ database which ranks the extent to which countries‘ legal and regulatory environment and uses the results to construct an index of ―weak investor protection‖.<sup>14</sup> A low value of this index reflects weak protection of investors‘ rights. A

> 11 We focus on rainfed cultivation as issues related to riparian rights and seasonal availability of water in a certain area as well as the investment needs for irrigation would require a more in-depth treatment.

> 12 Our measure of agricultural land outside the forest and protected areas is constructed from various bases, including Global Land Cover 2000 (http://www-gem.jrc.it/glc2000) PAGE Global Agricultural Extent (http://www. ifpri.org / dataset / pilot-analysis-global-Ecosystems-page), Global Forest Resources Assessment 2000 (http://www.fao.org/forestry/32203/en) and World Database on Protected Areas 2009 (http://www.wdpa.org / download.aspx). The extent of arable land outside forest and protected areas and in sparsely populated areas (that is to say less than 25 inhabitants per km2 or more than 4 ha
with those whose HDI scores are higher or lower. We interpret results on HDI variation with caution, as the gridded HDI data by Kummu et al. (2018) display very limited subnational variation.

# **4.3 ‘Strategic’ location choice**

In this section, we explore the notion that location choices for polluting power plants are not made at random. Case study evidence, especially from the U.S., documents that low-income and ethnic minority communities are disproportionately exposed to air pollution. We analyze the relationship between income and air pollution for over 1.3 million unique locations (pixels) around over 3,800 coal plants. We find that power plant locations are indeed not evenly distributed across income groups. In the proximity of coal plants, downwind areas are systematically poorer than upwind areas. Poorer communities are disproportionately exposed to toxic pollution from coal plants.

This pattern could be explained by several factors: When deciding plant locations, operators may strategically select spots where prevailing wind directions mean that pollutants are carried to lower-income communities. These communities are likely to have lower bargaining power to oppose such siting decisions. This regressive environmental burden may be further aggravated by spatial sorting once the plant assumes operations. As environmental quality (including air quality) is priced into land values, high-income households are able to sort into cleaner upwind areas, while low-income households are more likely to move into cheaper, more polluted areas.

To assess systematic inequalities in pollution exposure, we conduct a single difference analysis designed to test if plants’ downwind directions are associated with lower income levels. We estimate the following model: where the sample is at the plant-pixel level. We use the year when plants started to operate to code dependent variables, where start years differ across plants. In the first stage, we use SO2 at recipient pixel _j_ near plant _i_ . Our sample consists of pixels that are located within a 0.5-degree radius around plants, and located in up- or downwind locations. Pixels in crosswind directions