rafmacalaba/data-use-mentions-extended huggingface dataset:
across sectors to the CGE model based on the GTAP database.. The Institute for Applied System Analysis (IIASA) reports PM10 and other secondary emissions for India, which corresponded to almost to 8.7 million tons in 2010. The emission coefficients based on Garbaccio et al. (2000) were updated to reproduce the aggregate PM10 and other small particles level for India in 2005 and then extrapolated following the growth assumptions in BAU.

25. Table 3 in Annex 1 represents the relative shares of PM10 emissions by sector and energy (αi,j).

26. Emissions from productive activities and the respective coefficients (β _i_ ) were calculated as follows:

- First, the shares of production activities and process (XP) and energy use (C) related emissions in total emissions (E) were calculated as per the Garbaccio et al. (2000) study.

- Second, sector-specific emission coefficients in Garbaccio et al. (2000) were readjusted according to the GTAP classification in proportion to the sector‘s contribution to overall PM10 emissions and overall emission estimates from the IIASA model.

27. On the basis of equation (1) and the CGE simulations, the increase in PM10 emissions and other particulate emissions over time was calculated as a function of the demand for each type of energy by sectors (C _i,j_ ), and the economic expansion of production activities (XP _i_ ).

28. Second, the emissions coefficients αij and βi are modified over time to account for the improvements in the emission-capturing technologies, ; through (a) a shift to cleaner coal (imported coal has lower emissions per unit of energy than domestic coal and its share in the total amount of coal used in India is rising); and (b) other measures such as coal washing. These reductions in emissions are partly driven by administrative measures, and partly by trade factors and such improvements are included in the BAU. The rates of decline in unit emission are for these reasons taken from micro studies (see
developing economies; (ii) sales or downscaling of noncore, nondomestic businesses in host economies; (iii) deleveraging by subsidiaries and branches of foreign banks faced with reduced funding flows from parents, or parent attempts to transfer dividends, capital, or liquidity to headquarters; and (iv) increased cost of borrowing for subsidiaries, either as a result of a general worsening of funding conditions or as investor concerns about parents generate anxiety about the overall health of banking groups.

**25. Accordingly, a country’s vulnerability to European bank deleveraging depends on a combination of factors.** These include: (i) the size of cross-border claims of European banks relative to the recipient‘s economy, particularly where local affiliates play a key role in the

> 12 Based on an analysis of 161 large corporates, a Fitch report found that European companies increased their reliance on bond markets since the crisis, as European banks faced a multitude of market and regulatory pressures, resulting in a reluctance to lend to companies without markedly higher fees and margins charged on loans. A more difficult banking environment, coupled with a growing investor appetite for bonds, have, in turn, made bond issuance more attractive, with the median share of bonds in total debt rising from 51 percent in 2008 to 69 percent in 2010.
such simple forms of governance, it can be costly in terms of foregone profitable opportunities requiring more complex contracting arrangements.

Transactions that involve complex products and substantial quantities, which often require inter-temporal arrangements, such as ordering in advance, invoicing, and supplier credit, face significant difficulties in SSA. First off, there is the problem of high variation in product quality. Low standardization in production in much of manufacturing – producers are often “job-shop” manufacturers, making one-off products, rather than “assembly-line” producers of standard products – and unevenness in skill levels across firms increase the likelihood of quality variation in production. Moreover, the small number of upstream suppliers in the market for many products means that buyers cannot be confident that competition will have ensured the quality of various suppliers. Such factors compel buyers to perform costly inspections of orders. Second, searching out and trying different suppliers is difficult and costly in the SSA environment where information is limited, communication is difficult, and infrastructure is poor. Third, limited information about businesses and consumers, poor communications, and the fact that many small firms do not have fixed business sites, makes it relatively easy for delinquent clients to renege on their accounts. Vetting clients for supplier credit is thus difficult and offering credit is risky. Consequently, supplier credit and invoicing are reserved for transactions with larger, well-known clients.

Legal and judicial systems in the region are plagued by antiquated laws and procedures, insufficient human and material resources, poor management, and corruption.<sup>3</sup> These problems have resulted in extensive case backlogs and long delays, high costs, and a public perception of the legal and judicial system as too costly, unworkable, and corrupt for resolution of most commercial disputes. Of course, the sophistication and quality of legal institutions varies somewhat across countries and there are cases in the RPED surveys where businesses have taken legal disputes to court.

> 3 The Global Competitiveness Report of the World Economic Forum, 2005 ranks the quality of SSA’s business
Banking sector vulnerabilities could be compounded by a sluggish economy. Some countries have a negative forecasted real GDP growth for year 2021 of more than 5 percent which may have repercussions for bank resilience (Figure 2, Panel C). A weak macroeconomy may weigh negatively on the banking sector, setting off a negative feedback loop between lackluster financial sector performance and a weakening real economy.

The government fiscal position also has implications for the stability of banking systems, and vice versa. Economies with weak public balance sheets may find it difficult to provide policy support and maintain a credible backstop for the banking sector, which could also increase funding costs. In turn, weak banks may put sovereign debt sustainability in doubt and contribute to the bank-sovereign “doom loop” (Feyen & Zuccardi, 2019). Some countries are forecasted to approach 100 percent general gross government debt to GDP (Figure 2, Panel D), suggesting potential future vulnerabilities arising from the bank-sovereign nexus.

To assess whether the CDBP correlates with other indicators of banking systems' stability, we run crosssectional regressions and relate the CDBP with a set of core financial soundness indicators (FSIs).<sup>13</sup> A summary of the values of CDBP and FSIs across regions appears in Table 2. The Sub-Saharan Africa region (SSA) shows the highest un-weighted values of the CDBP, the regulatory Tier1 capital ratio, and liquid assets to short-term liabilities. South Asia (SAR) is the worst performing in terms of CDBP, non-performing loans ratio and exposure to changes in foreign exchange. Latin America and the Caribbean (LAC) has the lowest capital ratios, and MENA has the lowest liquidity ratios.

**Table 2. Consolidated distance to breakpoint and selected FSI indicators (2020; unweighted median and median weighted by GDP at market prices)**

||**Conso**<br>**Distance**<br>**Point (C**<br>**poi**<br>|**
Urbanization_ (New Delhi: Oxford University Press, 2005).

- Sridhar, Kala Seetharam, “Impact of Land Use Regulations: Evidence from India's Cities,” _Urban Studies_ , 47(7), (2010): 1541-1569.

- Srivastava, Vivek, Pooja Gupta and Arindam Datta, “The Impact of India’s Economic Reforms on Industrial Productivity, Efficiency and Competitiveness: A Panel Study of Indian Companies, 1980-97,” National Council of Applied Economic Research Report (2001).

- United Nations, _World Urbanisation Prospects: The 1999 Revision_ (New York: United Nations Population Division, 2001).

World Bank, “Reshaping Economic Geography,” World Development Report (2009).

- World Bank, “World Bank Urban and Local Government Strategy”, Urban Development Unit (2011a).

- World Bank, “Perspectives on Poverty in India: Stylized Facts from Survey Data,” Economic Policy and Poverty Sector (2011b).

24
we rely on customs data covering the universe of firm-level export and import transactions from Servicio Nacional de Aduana. We merge the customs data with the worker panel database based on a common firm identifier. We derive a firm panel database from the IESS worker panel database where, for each firm, labor market outcome variables are constructed by aggregating across all of its workers in a given year. This firm panel database includes all firms that export at least once during the 2006-2017 period.<sup>47</sup> In addition, we use the Ecuadorian firm registry (Sistema de Rentas Internas del Ecuador (SRI)) collected by the Superintendence of Companies for the 2007-2017 period merged with

> 45Access to the IESS employer-employee database is possible only in the National Statistical Institute’s premises in Quito hence research based on this database is rare. To our knowledge the only other paper using this database is Mu ̃noz S et al. (2018)

> 46The tradables sector is defined as the set of industries with 2-digit codes less than equal to 33 in ISIC revision 4 classification. Observations with zero months worked correspond to the worker in Ecuador not having a formal job but we have no information as to whether the worker is unemployed, self-employed, working in an informal job, or out of the labor force, a caveat of the IESS data. Given the much smaller size of the labor force in Ecuador than Brazil there is no need to conduct the analysis for a random sample of workers. We exclude from the final sample for Ecuador outliers defined as workers whose monthly wages are higher than 50,000 USD.

> 47For these firms the panel includes all years of data (even years when the firm is not exporting).

55
Table 5: Initial wage differentials, individual level data, 1996

|Dep. Var.: Log(Income)|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Black (=1)|-1.002***|-0.509***|-0.553***|-0.590***|
||[0.004]|[0.003]|[0.004]|[0.004]|
|Mining (=1)|0.489***|0.374***|0.426***|0.417***|
||[0.012]|[0.010]|[0.010]|[0.011]|
|Black*Mining|-0.247***|-0.082***|-0.093***|-0.114***|
||[0.013]|[0.011]|[0.011]|[0.011]|
|Observations|378,125|357,240|357,240|357,240|
|R-squared|0.188|0.49|0.504|0.529|
|Individual controls|-|Yes|Yes|Yes|
|Province FE|-|-|Yes|-|
|MunicipalityFE|-|-|-|Yes|

Notes: The estimation method is OLS. Data from the 1996 population census 10% sample. Sample of males between 15 and 65 years old, employed in firm/company (not self-employed or family employed). Individual controls include age, age squared, indicators for highest achieved education, and occupation indicators. Robust standard errors
provinces.

In each division, relatively poor municipalities (school districts) were chosen first. Municipalities of census-2000 income classes ranked 3 to 5 (the highest income is rank 1 and the poorest is rank 6) were chosen from the adjacent area (near the division border) of an intervention and a nonintervention division (see Philippines, Department of Finance 2001). In Ifugao division,

> 21As described in Section 5, we use wages received at the latest job held. This mitigates the above selection problem since we have wage observations for those who are currently studying (or are unemployed) but recently had work experience.

15
We further examine the validity of this interpretation by exploring the effect of PM2 _._ 5 on children’s health.<sup>33</sup> To do so, we use data on self-reported children morbidity from the DHS. Our main outcome is the presence of cough accompanied with short breath. This is a common indicator of acute respiratory disease. As a falsification test, we also estimate the effect of PM2 _._ 5 on other diseases, not linked to short term exposure to air pollution, such as fever, diarrhea, and anemia. Our empirical specification is similar to (1) but there are some changes due to the limitations of the DHS dataset.<sup>34</sup> In particular, we include as covariates month and year fixed effects, weather controls, and children and mother’s characteristics, but do not include household, week, or municipality-by-year fixed effects.<sup>35</sup> As measures of exposure to pollution we use an indicator of average PM2 _._ 5 exceeding the U.S. standard and the log of average PM2 _._ 5.<sup>36</sup> Table 3 displays the results. Consistent with the existing literature, we find a positive and significant relationship between exposure to PM2 _._ 5 and our indicator of acute respiratory disease, but there is no significant relationship with other non-respiratory diseases. The magnitude of this relationship is sizable. According to our results, reducing PM2 _._ 5 to the compliance level with the U.S. standards would lead to a 9 percent reduction in the incidence of acute respiratory diseases. This represents almost half the average incidence.

# **4.2 Additional checks**

Our identification assumption would be violated in the presence of confounding, unobserved, omitted determinants of labor supply that vary over time within census blocks. In other words, if there were omitted variables that have not been fully accounted for by the rich set
2006 to 2016.<sup>12</sup> In 2012, the registry adopted a panel structure with unique firm and worker identifiers that allow both firms and workers to be followed over time.

The registry includes very detailed information at the firm level. It has rich information on financial statements—net sales, asset and liability statements, debt, and profits—among firms that pay institutional and corporate taxes. The database also contains records on capital and machinery stocks, capital expenditures, labor costs, exports and imports, and output and income. The data also include standard information on age, the economic sector at the four-digit level, and the geographical location of firms up to the district level.<sup>13</sup> At the worker level, the registry includes individual-level information on workers who are registered with the Turkish Social Security Institution and employed in those firms. As of 2016, the EIS database included about 12.6 million workers, representing the universe of formal wage workers in nonfinancial sectors, and about 80 percent of all wage workers.<sup>14</sup> Information on the age, gender, occupation, days worked, and wages is available for each registered worker. This allows us to calculate total employment and the total wage bill for each registered firm by quarter.

A first major advantage of using the EIS database to study the impacts of the minimum wage is that it has no minimum threshold for firm size. This is a critical feature for analyzing firm responses to the minimum wage, especially in the context of a developing economy in which small firms predominate. In Turkey, 50 percent of all registered firms have at most two employees, and 75 percent have at most five employees. These firms typically pay low wages, and, as a result they are more exposed to increases in the minimum wage. A second attractive feature of the EIS is the broad coverage of sectors of activity: it includes registered private firms in all sectors of activity, apart
account for sample design effects among the residuals, which would increase the estimated mean squared errors and improve coverage rates.<sup>43</sup> In addition, benchmarking could be incorporated directly into the bootstrap procedure, to obtain more accurate estimates of benchmarked mean squared errors. Third, it should be fairly straightforward to incorporate the ordered quantile normalization transformation directly to the R EMDI package, which already allows for Box-Cox and log shift transformations. This would enable the software to estimate poverty gaps and severity in addition to headcount rates when using the ordered quantile normalization transformation.

This study only considered a measure of non-monetary poverty, in order to evaluate the results of the area predictions against actual census data. This raises the important question of the extent to which the results generalize to predicting monetary poverty rates. The types of geospatial indicators considered in this study are moderately less predictive of monetary welfare than non-monetary welfare. For example, when predicting district-level monetary poverty in Tanzania using the same geospatial indicators considered above, the geospatial variables selected by a stepwise procedure explained 23.5 percent of the variation in household per capita consumption, versus 30 percent for a lasso-selected model of non-monetary welfare.<sup>44</sup> This reflects the difficulty of predicting both transient household welfare shocks and measurement error, both of which are present to a greater degree in measures of monetary poverty than non-monetary poverty. It is encouraging that the household-level EBP model continues to perform well when noise is added to non-monetary welfare. The efficiency gain from using the household model was notably smaller in Sri Lanka when using the noisier welfare measure, but even in that context incorporating geospatial data led to an increase in precision roughly equivalent to doubling the sample size. These results are only suggestive, however, and an important outstanding research agenda is to compare small area estimates of monetary poverty obtained using geospatial data to those obtained from standard census-based poverty maps directly from census data. This can confirm that the encouraging results presented above apply to monetary poverty.

A final
15 and 65.

**Table 3: The share of industry in total employment is increasing slowly**

||Share in total labor<br>force in 92/93 (%)|Share in total labor<br>force in 97/98 (%)|Change<br>92/93-<br>97/98|
|---|---|---|---|
|Agriculture|71.2|66.4|-4.7|
|<br>Industry|11.8|13.1|+1.3|
|<br>Industry less construction|10.3|10.7|+0.4|
|<br>Services|17.0|20.5|+3.4|

_Source:_ VLSS I and 2 Table 4 shows that over the period 1993-97 economic growth has raised the country's real value-added per worker by 39.8%. In Industry, strong growth coupled with weak employment growth has been accompanied by a large 54% increase in average labor productivity. In agriculture and services, value-added per worker has increased by respectively 20.8% and 19.2%. Thus, the productivity gap between industry and other sectors has sharply widened. In only five years, agricultural labor productivity has declined from about 27% of industrial levels to less than 21%.

I Note however that the VLSS employment data used in this section provides a less somber picture than employment data from Vietnam's General Statistical Office (GSO, Statistical Yearbooks). According to GSO data, the average annual growth of industrial employment was 3.1% instead of 4.0% as indicated by VLSS data. This would represent only 23% of the rate of industrial growth. In addition, when construction isomitted GSO indicates employment growth of only 1.7% or 13% of this sector's GDP growth. Official data from
<mark>The next two sections s</mark> ummarize the empirical literature on climate change and migration and identify the main evidence and data gaps that could be closed by leveraging existing multi-topic household surveys as a starting point.

# **2.2 The status of the literature on climate change and migration**

In a recent data-oriented synthesis of the current state of empirical climate migration literature, Letta et al. (2023) notice that while the literature has grown exponentially in the last decades, this growth has not resulted in definitive evidence regarding the nature and magnitude of climate-migration nexus, nor in a clear-cut identification of transmission channels. There are two main reasons for this lack of correspondence between research output and conclusive evidence. The first is the nature of the subject matter itself: migration is per se an irreducibly complex and context-specific phenomenon and, when coupling it with the vast uncertainties surrounding the effects of climate change, it becomes clear how difficult it is to distinguish climate migrants from other types of migrants. The second, as already stated, is the existence of severe data gaps in developing contexts, especially at the microeconomic, longitudinal level. While precise figures of migratory flows induced by climate change are not likely possible, the literature equips us with four important and policy-relevant qualitative insights.

First, **evidence-based projections are needed** . Currently, there is no empirical evidence to support projections of sudden and massive migratory flows as a response to climate change, especially when referring to international migration. Instead, the available evidence suggests that climate-induced immobility, _i.e._ , a _decrease_ in migration in response to climatic shocks, is at least as important as climate-related migration. Immobility appears to be driven by the existence of liquidity constraints generated or aggravated by climate change, which makes it impossible for the poorest households to migrate in response to the shock. From this perspective, better-off households can choose who migrates in order to diversify risk and, as such, can be considered more resilient than those
the value added of services, consumer goods industry, and agriculture (“rest of the economy”) increase because the additional revenue generated by domestic carbon taxes increase households’ consumption and savings. This helps less carbon-intensive sectors producing final consumers’ goods (e.g. agriculture) and services. Higher households’ consumption does not help heavy industry which is producing mainly capital and energy intensive intermediate goods. Yet, despite helping the relatively knowledge-intensive “rest of the economy”, asset diversification in our simulations does not change the core specialization of FFDCs in the oil, gas, and coal product space. In three of the four FFDCs sub-groups in figure 12, fuel extractive sectors and carbon intensive heavy industry dominate exports even in 2050. The decline of the share of commodity exports is offset by the increase of the exports share of carbon intensive manufacturing, especially in traditional diversification scenarios. Only for the low-income countries with large unexploited fuel reserves (figure 12d), the two diversification strategy choices have a significant bearing on the future economic structure, because their “rest of the economy” sectors have played a major role already in the base year’s exports. Many of these countries are currently more dependent on exports of agricultural commodities than fossil fuels.<sup>7</sup>

> 7 These results can be partly attributed to the nature of the CGE models, which are not very good in simulating the rapid growth of new sectors, which are insignificant in the economy in the base year, for which the BAU was calibrated with input-output data for 2011.

20