rafmacalaba/data-use-mentions-extended huggingface dataset:
# _Other variables_

The independent variables encompass GDP per capita, labor productivity per worker, and several distinct methodologies for disaggregating GDP and labor productivity. GDP per capita data, measured in US dollars at constant 2015 prices, was sourced from the World Development Indicators (World Bank, 2024).

Productivity variables are drawn from the Cross-Country Database of Productivity developed by Dieppe (2021). The first productivity variable is labor productivity, defined as the amount of output produced per worker and calculated as GDP divided by the number of employed people.

Labor productivity growth is further decomposed into three components: human capital growth, capital deepening growth, and total factor productivity (TFP) growth. Human capital growth measures how improvements in worker skills and capabilities through education, training, and health improvements increase productivity. Capital deepening measures changes in the capital-toworker ratio, and TFP measures the efficiency with which factor inputs are combined. TFP often serves as a proxy for technological progress in growth accounting exercises (Dieppe, 2021).

We further disentangled the components of economic growth from an expenditure perspective and calculated per capita values for consumption, (public and private) investment, government spending, exports, and imports measured in constant 2015 U.S. dollars. These measures were derived from the World Development Indicators database.

Moreover, we delineated economic growth into sustainable and unsustainable components, adopting the methodology outlined by Mahler (2021). The unsustainable part of national income was quantified as the aggregate of natural resource depletion, the economic costs attributable to CO2 and particulate emissions, and the consumption of fixed capital. Conversely, the sustainable portion was determined by subtracting these costs from the total national income, represented as Gross National Income (GNI).

# _Data structure_

The data is organized into spells, as defined by Cox (2007). These spells represent the intervals between two survey years, with the length of each spell determined by the time elapsed between the surveys. Within this framework, the dependent and most independent variables are computed as the average
for<br>international marketingin substantial volumes.|
|Processed Materi-<br>als|Materials that have been transformed or refined from basic raw materials as an intermedi-<br>ate step in the manufacturing process. Processed materials include steel, glass and cement.<br>In this link of the supply chain, value added comes from processing raw materials into pre-<br>cursors that can be easily transported, stored and used for downstream subcomponent<br>fabrication.|
|Subcomponents|Unique constituent parts or elements that contribute to a finished product. Clean energy<br>technology examples include generation sets for wind turbines and crystalline wafers for<br>crystalline silicon PV modules. Note that what is considered a component by the manufac-<br>turer may be considered the finished product by its supplier. In this link of the supply chain,<br>value added comes from fabricating processed materials into subcomponents that can<br>then be assembled(with other subcomponents)into endproducts|
|End Products|The finished product of the manufacturing process, assembled from subcomponents and<br>readyfor sale to customers as a completed item. Clean energyexamples include|

5 The analysis of value chain relationships, such as determining which input products are used to make other downstream products, generally requires the use of input-output tables or supply chain data. Unfortunately, consistent and comparable input-output or supply chain data is not presently available for products at the 6-digit HS classification. For example, the World Input Output Database (WIOD) currently only covers 43 countries and 56 sectors identified under the International Standard Industrial Classification (ISIC Rev. 4). Only a handful of these countries are developing countries and while a mapping exists from the HS classification to the ISIC classification, too much information is lost when trying to aggregate one classification with around 5,000 products to another with 56 industries. An alternative approach that is sometimes used in the literature is to draw on more detailed input-output data that are
- _Lender types_ : whether the lender is a state bank, a private bank or a non-bank institution (which include microfinance institutions, credit cooperative, credit unions or finance companies). The expectation is that a non-bank institution would be less likely to require collateral because it is less formal and more likely to operate on trust and reputation.

Country characteristics:

- _GDP per capita_ : this measure captures the overall economic development of countries and is in real international dollars (PPP) based on data from the World Economic Outlook.

- _Institutional variables:_ We use _Regulatory Quality_ and _Rule of law_<sup>6</sup> data from The World Bank’s Governance Indicators.

- _Financial development measures:_ Our focus is on the availability of credit information, and our proxy is the _Depth of Credit Information_<sup>_7_</sup> (which takes a value of 1 to 6), taken from the World Bank’s Doing Business Surveys. We also use two common financial development measures. The first one is domestic credit to private sector, as a % of GDP.<sup>8</sup> This proxy is widely used in literature on the access to finance (for example, see Beck et al, 2006). The second measure is interest rate spread, to capture the efficiency of the banking sector<sup>9</sup> .

The first column in Table 1 presents the OLS regressions with country fixed effects. The dependent variable takes the value 100 when the loan is collateralized and zero otherwise. Relevant predictors of collateralized borrowing are years in operation, firm size, industry, having international management certification, export orientation, firm’s ownership and having overdraft facility. Essentially, we look for _within-country variations across firms_ . Columns 2 to 6 control for country characteristics (and remove country fixed effects). Overall, older firms are less likely to pledge collateral. One more year of operation reduces the chance of pledging collateral by about 0.1%
# **3.2** **_Survey data_**

- Survey data from the Demographic and Health Survey (DHS) were used to examine child

- marriage. We reviewed the most recent standard or interim DHS survey for each country that had GPS identifiers available for the DHS. Countries were then selected based on: • Country had at least 100 conflict deaths (per UCDP) within the five years preceding the survey (the most recent standard or interim DHS survey with GPS). This criterion ensures that countries are substantially conflict-affected and also that there is enough variation for our identification strategy, discussed below.

- A rate of child marriage from the survey in question (the most recent standard or interim DHS with GPS) above 10%.

These selection criteria yielded a sample of nineteen countries: Bangladesh (2017-18); Burundi (2016-17); Cameroon (2018); Chad (2014-15); Colombia (2010); Côte d’Ivoire (2011-12); Democratic Republic of the Congo (2013-14); Arab Republic of Egypt (2014); Ethiopia (2016); Guinea (2018); India (2015-16); Indonesia (2002-3); Kenya (2014); Mali (2018); Myanmar (2015-16); Nigeria (2018); Pakistan (2017-18); Peru (2009); and Philippines (2017).<sup>6</sup>

# **_3.3 Geospatial boundaries data_**

To match conflicts and DHS clusters with locations, we relied on the Database of Global Administrative Areas (GADM) version 3.6 files. We downloaded the boundary files for the second level of administrative geography for each country.<sup>7</sup> Administrative boundaries were based on 2020 data; boundaries may have been different at the time of fielding, but using a constant set of boundaries is important to consistently measure conflict. It is, however, important to note that second level administrative geographies vary in size and number across countries,
Table 4 reports analogous estimates, but allowing for a 1-year lag in the transmission of green technologies (as measured by mentions in the corresponding ECTs). The evidence shows a broadly similar pattern with positive and significant coefficients for the variables of interest. The sole exception for the coefficient on the interaction term in column (8), which suggests that when firms are simultaneously buyers and suppliers of a green firm, they are significantly likely to become green irrespective of the level of emissions of the green firm to which they were linked.

Since firms may have various types of linkages at the same time, it may be difficult to isolate the role of each type of linkage when considering the full sample. For robustness, in Table 5 we examine if the baseline results are sensitive to the exclusion of firms with multiple linkages. Once again, we observe that the results remain qualitatively very similar.

The majority of firms in our sample have affiliates in other countries and hence are multinational enterprises. In Table 6 we restrict the sample to include only linkages with MNEs. Once again we observe qualitatively similar results. In Table 7, we consider only linkages with non-MNEs. In this case, we are left with a relatively small number of observations, but we still observe positive and significant coefficients for most linkages considered.

In Table 8 we consider instead a sub-sample in which the firm that is linked to the green firm is a MNE, while the green firm could be or not an MNE. Once again, the baseline results remain valid. Finally, in Table 9 we consider a sub-sample in which the linked firm is not an MNE, while the green firm may or not be an MNE. In this case, we are again left with a relatively small number of observations, but we observe once again a positive and significant coefficient for most linkages considered.

Taken together, these estimates suggest that firm-to-firm linkages play an important role in the transmission of green technologies across multinational enterprises, especially when the green firm had high emissions to
, T. (2020). The global dataset of historical yields for major crops 1981– 2016.</mark> _<mark>Scientific Data</mark>_ <mark>,</mark> _<mark>7</mark>_ <mark>(1), 1–</mark> 7.

- <mark>Kalkuhl, M., & Wenz, L. (2020). The impact of climate conditions on economic production. Evidence from a global panel of regions.</mark> _<mark>Journal of Environmental Economics and Management</mark>_ <mark>,</mark> _<mark>103</mark>_ <mark>, 102360.</mark>

- <mark>Kummu, M., Taka, M., & Guillaume, J. H. (2018). Gridded global datasets for gross domestic product and Human Development Index over 1990–2015.</mark> _<mark>Scientific Data</mark>_ <mark>,</mark> _<mark>5</mark>_ <mark>(1), 1–15.</mark>

66
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# **1 Introduction**

It was not until recently that the downsides of intensified industrialization have started gaining academic attention. One of the burgeoning topics is how increasingly frequent and severe industrial disasters have taken place around the world. Since the 1990s, the number of documented large-scale industrial disasters has increased by nearly fivefold (EM-DAT, 2017). According to the International Disaster Database from the Centre for Research on the Epidemiology of Disasters (CRED), the types of industrial disasters that nations experience include gas leaks, oil spills, nuclear explosions, and chemical contamination. These incidents often lead to disastrous environmental consequences with impacts felt for years. Developing countries, with laxer environmental standards and a strong desire to promote industries and attract foreign investment, are most likely to bear the brunt of these industrial disasters. Ironically, these countries usually lack the capacity to fully evaluate the causes and effects of disasters, hold perpetrators accountable, and provide timely assistance to the affected population. Existing studies on the effects of man-made environmental disasters in developing countries, due to capacity and budget constraints, and sometimes political sensitivities, are rare.

In this paper, we examine the labor-market impacts of _Formosa_ ,<sup>1</sup> an industrial marine pollution crisis breaking out in Vietnam that devastated the ecosystem and disrupted fishery activities in the country’s central coast in 2016. Our empirical analysis leverages a novel source of high-resolution satellite data on night-time boat detection in Vietnam’s marine exclusive economic zone (EEZ), and relates it to employment data from Vietnamese labor force surveys. Exploiting both the industry-specific and location-specific natures of the _Formosa_ shock, our identification strategy compares fishery workers that lived in the affected region to both non-fishery workers in the same area and other fishery workers outside the affected zone.

We estimate the impact of _Formosa_ by employing a series of difference-in-differences estimations (DiD) using individual-worker data, and show that the disaster sharply reduced average fishery income by as much as 42 percent in the rest of 2016. Utilizing high-resolution satellite
- 131 -

The change of dependent variable to an hourly basis, however, had major effects on the coefficients and their significance. It reduced the estimated coefficient and t-value of primary education and increased that of other levels of education. It also increased coefficients and significance of all diploma variables. Clearly, two of VV's conclusions, viz. the sharp progression of returns to education with level of education and the importance of diplomas for earnings, critically depend upon their choice of hourly wage as dependent variable. Using monthly wage data, this paper has shown that the increase of the effect of education on earnings with level of education is much milder and that diplomas generally do not affect earnings, except in the formal private sector in other cities.
document negative economic effects from certain U.S. environmental policies. While not directly related to firm productivity, X. Cai et al. (2016) show that stricter environmental regulations in China reduced foreign direct investment except for investments originating in countries with stronger environmental protections than China. Keller and Levinson (2002) find similar results across U.S. states. These results underline the importance of balancing climate mitigation goals with goals for economic growth and development in LMICs.

Climate mitigation policies may also carry distortionary effects that direct estimates of policies on firm productivity do not capture. For instance, S. P. Ryan (2012) shows that environmental regulations can increase barriers to entry, thereby reducing competitiveness in certain industries. Accounting for both direct costs and reduced competition substantially changes estimates of the welfare impacts of climate mitigation policies. In many LMICs, climate mitigation policies may also encourage informality. Such considerations highlight potential distributional impacts of mitigation policies on firms.

There is a marked absence of evidence on the economic impacts of climate mitigation policies outside the global north and China. The limited evidence base from LMICs impacts our ability to understand whether and why specific climate mitigation policies will slow economic growth in different contexts.<sup>11</sup> Given the importance of economic development for LMICs, it is critical to build more evidence on climate mitigation policies that at best complement, and at a minimum do not detract from economic growth.

# **4 Conclusion**

It is worth noting that of the of the 106 papers we identified that study firms in LMICs, 6 provide evidence from RCTs, 46 provide empirical evidence from natural experiments, 11 estimate structural models, and the remainder are descriptive, theoretical, or overview

> 11Bassi et al. (2022) show that Ugandan firms would significantly reduce profits if they were to relocate away from polluted roads, but their work does not directly study a policy to encourage such movements.

33
contrast, export times in Sub-Saharan Africa are especially long, taking 471⁄2 days on average. In addition, the variation across countries in Sub-Saharan Africa is large, ranging from 16 days in Mauritius to 116 days in the Central African Republic.

The time delays reported in the survey are probably at the lower end of the time it takes to move the average product from factory to ship. This is because the products are chosen so that they do not require cooling or any technical inspections based on use of hazardous materials.

Table 2 presents correlations for the full sample of bilateral trade between the time to move goods from factory to ship and other variables included in the regression analysis. Time is positively correlated with remoteness, more remote countries tend to have longer times of getting goods to ship. Time is negatively correlated with per capita income, implying that wealthier countries tend to have better trade facilitation.

The trade data are from the UN Comtrade database. GDP and GDP per capita are from the World Bank’s World Development indicators. We use data for 2001-2003, convert to constant values, and average them in order to avoid idiosyncracies in any given year, though results are very similar if we use only data for 2003 (the latest available). Trade data were not available for 20 of the 146 countries for which we have data on the time to move goods from factory to ship, so our final sample is the 126 countries listed in Table A1.<sup>11</sup>

> 11 The 20 countries with missing trade data for 2001-2003 are Bhutan, Chad, Congo Republic, Democratic Republic of Congo, Ethiopia, Haiti, Iraq, Laos, Macedonia, Mauritania, Palau, Puerto Rico, Serbia and Montenegro, Sudan, Taiwan (China), Timor-Leste, Tonga, Vanuatu, Vietnam and West Bank and Gaza.

10
Engle and Yoo (1987) “Forecasting and Testing in Cointegrated Series”, Journal of Econometrics, vol. 35, pp. 143-159.

Engle, R. and C. Granger (1987) “Cointegration and Error Correction: Representation, Estimation and Testing”, Econometrica vol. 55, No. 1, pp. 251-276.

Friedman, J. and J. Levinsohn (2002). “The Distributional Impacts of Indonesia’s Financial Crisis on Household Welfare: A Rapid Response Methodology”, _The World Bank Economic Review_ , vol. 16, No. 3, pp. 397-423.

Johansen (1988) “Statistical Analysis of Cointegrating Vectors”, Journal of Economic Dynamics and Controls, vol. 12, No. 3, pp. 231-254.

National Bureau of Statistics (2011) “South Sudan Cost to Market Report”, August 2011.

Stock, J. & M. Watson (1993). "A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems," Econometrica, vol. 61, No. 4, pp. 783-820.

Timmer (1986) “Getting Prices Right: The Scope and Limits of Agricultural Price Policy. Ithaca: Cornell University Press.

Timmer, C.P. (1996) “Does Bulog stabilize rice prices in Indonesia? Should it Try?” Bulletin of Indonesian Economic Studies, vol. 32 No. 2, pp. 45-74 Varela, G., E. Aldaz-Carroll and L. Iacovone (2012) “Determinants of Market Integration and Price Transmission in Indonesia” Journal of Southeast Asian Economies, vol. 30 No. 1, pp. 1944.

WFP (2014) “ FAO/WFP Crop and Food Security Assessment Mission to South Sudan” Special Report.

The World Bank (2011), “A Poverty Profile for the Southern States of Sudan”. Africa Region, mimeo The World Bank. 2012b. “Agricultural Potential, Rural Roads, and Farm Competitiveness in South Sudan”.

World Bank (2014), “South Sudan
CEQ Assessment is that employer contributions to pensions are borne by the employee in the form of lower wages—that is, the analysis allocates the total pension rate to employees, including both employee and employer’s contributions. The survey identifies roughly 140,000 pension contributors tax resident in the West Bank and Gaza - close to the numbers reported in the latest administrative data of 155,000 contributors.<sup>36</sup>

**There are three major pension schemes in the West Bank and Gaza, and one in Israel.** In the West Bank and Gaza the unified pension scheme applies to individuals below the age of 55 as of 2016, while the Gaza civil servant pension scheme and the West Bank civil servant pension scheme variously apply to pension contributors that were above age 55 in 2016 (the former in Gaza and the latter in the West Bank). All pension contributors in the survey were below age 55 and so the unified pension scheme was applied to all directly identified pension contributors working in the West Bank and Gaza in the PECS 2015/16.<sup>37</sup> There are three components: (i) a defined benefit component (9 percent paid by employer and 7 percent

> 35 Pensions can result in different types of redistribution. They can redistribute within the contributing population, for example by placing a floor and a cap on pension income, they can generate inter-distributional redistribution based on demographics and economic growth, they redistribute from workers who contribute but not enough to receive the benefits, and when the social security system is in a deficit, as is the case in the Palestinian Territories, they redistribute from tax-paying individuals to pension recipients.

> 36 World Bank (2016). Public Expenditure Review of the Palestinian Authority: Towards Enhanced Public Finance Management and Improved Fiscal Sustainability. World Bank Report No: ACS18454. [http://hdl.handle.net/10986/25100]. Administrative data of pension contributors from the Public Expenditure Review (PER
# **Table OA8. Welfare Outcomes using Propensity Score Matching**

||Percent of output<br>sold|Log total household<br>income|Food security<br>PCA index|
|---|---|---|---|
||(1)|(2)|(3)|
|_Panel A: Full Baseline Sample_||||
|Beneficiary|0.277*|-0.381|0.425|
||_0.054_|_0.348_|_0.187_|
|Lottery|0.324**|0.300|0.459|
||_0.023_|_0.514_|_0.178_|
|Lottery x Beneficiary|-0.117|0.155|-0.400|
||_0.495_|_0.815_|_0.320_|
|R-squared|||0.013|
|Mean of dep. var. for NB in NL vil.|0.090|12.7|-0.737|
|P-val of B = NB in L villages|_0.105_|_0.702_|_0.928_|
|Model|Tobit|Tobit|OLS|
|Percentage of censured observations|0.64|0.03||
|Number of households|381|260|427|
|_Panel B: Matched Baseline Sample_||||
|Beneficiary|0.377**|-0.695|0.279|
||_0.019_|_0.201_|_0.596_|
|Lottery|0.415**|-0.213|0.431|
||_0.011_|_0.634_|_0.393_|
|Lottery x Beneficiary|-0.270|0.527|-0.286|
||_0.171_|_0.517_|_0.594_|
|R-squared|||0