rafmacalaba/data-use-mentions-extended huggingface dataset:
|||||
|empoyers|MEX||||||||||||||
||PER||||||||||||||

Source: Own elaboration based on CAIT, OECD, and SEDLAC data.

# Green jobs: Occupations vs. sectors

By combining the sector and occupation classifications, four mutually exclusive job types (see Figure 1) are created. Each group faces two primary sources of vulnerabilities to green policies. First, workers in non-green sectors would be more vulnerable to policies that increase the cost of GHGE, such as a Carbon Border Adjustment Mechanism (CBAM).<sup>14</sup> If such a policy reduces the competitiveness of non-green sectors, they may be more likely to shrink and shed labor. Second, workers in non-green occupations would be more vulnerable in terms of lacking the skills to remain employed or find a new job if green policies create a skill-biased change in labor demand. By overlapping these two dimensions, the following four categories are created:

> 14 See <u>https://ec.europa.eu/commission/presscorner/detail/en/qanda_21_3661 the CBAM as proposed by the European</u> Commission.

15
the 2009 Financial Crisis elsewhere in the same year. We find that the true growth in output was closer to 19.89%. We also considered a grade C normalization for Zimbabwe, which subsequently weights GDP more (*). Even in this case, our estimated composite would still only be 29.81%. To explain this, we note that over 60% of Zimbabwe’s economy may be a shadow economy (Schneider and Enste, 2013). Our finding suggests that this segment of the economy may have responded more elastically to the 2009 global downturn than the measurable, formal economy.

**Conflict** . Figure 9 considers the Syrian Arab Republic and the Republic of Yemen, which are at or near the top of the list in Table 7, in Grades C and E respectively, and both endured civil war during this period. Conflict has direct consequences for macroeconomic performance (H ̈onig, 2021) and is also known to cause changes in NO2 (Lelieveld et al., 2015). Both in the Syrian Arab Republic over 2012-2013, and the Republic of Yemen in 2015, we find that actual output was likely higher than reported in GDP. This may be due to informal economies, characterized by unincorporated businesses that can not be counted in GDP, and which are known to arise during conflict (Peksen and Early, 2020; Galdo et al., 2021). It is especially

> 19The IMF’s estimate differs somewhat from the Penn World Tables, at -35%, closer to our estimate.

28
recessions. But their identification strategy requires data on government consumption forecast errors, which essentially limits their study to only OECD countries.

Our paper makes three contributions. First, by clearly distinguishing between the state of the business cycle and the fiscal position, we establish that the fiscal position is a unique state that determines the size of the fiscal multiplier. Second, we show the empirical relevance of the transmission mechanisms that underlay the state-dependent effects due to fiscal position: the Ricardian channel and the interest rate channel. Third, compared to previous studies, our sample includes a larger set of countries covering advanced and developing economies, thus providing a general result on the state-dependent effects due to fiscal position.

The rest of the paper is organized as follows. Section 2 presents the econometric methodology. Here, we discuss the IPVAR model, the identification strategy, and the database. We present estimates of state-dependent multipliers in Section 3. In Section 4, we discuss the transmission mechanisms that highlight the Ricardian and the interest rate channels. Section 5 discusses robustness exercises and Section 6 concludes.

# 2 Empirical Methodology

## 2.1 Econometric Model

A standard panel structural VAR (SVAR) estimates a single set of parameters which then yields an “average”or unconditional multiplier. Our objective is to go beyond the unconditional multiplier, and investigate how multipliers can depend on specific macroeconomic conditions, in particular fiscal position of governments. For that, we deploy the Interacted Panel Vector Autoregressive (IPVAR) model where the main innovation, with respect to a standard panel SVAR, is that the model coeffi cients vary deterministically according to conditioning (state) variables. Thus, the IPVAR results in a framework where model dynamics and hence, estimated multipliers are conditional on the state variables. By choosing the conditioning variable to be a measure of fiscal position in the IPVAR, we estimate multipliers that depend on fiscal position.

The IPVAR model, in its structural form, is represented by: where for a given country i in period t, gc represents real
Given the increasing interest and analysis on multi-product firms in trade, we provide in addition to the measures in the Database a set of companion matrix tables by country and year showing the distribution of exporters and of total exports by the number of products exported and the number of destination markets served. The matrix tables consider the following categories for the number of products and the number of destinations: 1, 2, 3, 4-10, 11-20, 21 or more.<sup>31</sup> Some examples of the matrix tables are provided in Appendix 4. All matrix tables can be downloaded at http://data.worldbank.org/data-catalog/exporter-dynamics-database.

To illustrate the content and diversity of measures included in the Database, Table 2 presents a summary of a representative set of measures in the Database –average and median exporter size, share of top 5 percent firms, number of exporters, number of HS 6-digit products exported per firm, number of destinations served per firm, entry exit and survival rates– as well as total exports per country. We focus on averages per country for the period 2006-2008 which is the period most commonly covered across countries (and captures trade performance before the global financial crisis that started at the end of 2008).<sup>32</sup> Table 3 presents the same set of measures in the Database but for groups of HS 2-digit sectors, where the measures for each sector group are obtained as averages across all countries that export that particular group of HS 2-digit sectors, again focusing on the period 2006-2008.

Some interesting cross-country patterns emerge from Table 2. First, there is tremendous variation across countries in the export base (number of exporters) and the average exports per firm. Developed countries tend to exhibit the larger numbers of exporters. Among developing countries, the largest numbers of exporters are found in Turkey and Mexico followed by South

> 31 These matrix tables are available only for the developing countries for which we have the customs exporter-level data. We are
|(0.27)|(0.35)|
|Mean number of houses in village with brick|0.298|0.159|
|walls|(0.46)|(0.11)|
|Mean number of houses in village with more|-0.355|-0.0584|
|than 5 rooms|(0.52)|(0.54)|
|Fraction of surveyed households with a|1.118|-0.274|
|<br>television|(0.41)|(0.17)|
|Observations|200|200|
|R-squared|0.78|0.69|

Note: The columns report OLS estimates of the association of measurable village characteristics with the number or share of community radio stations to which villages have access, controlling for commune fixed effects.

The village survey includes data on village distance from urban centers and to a train or bus stop; smaller and more remote villages could both be likely to have fewer accessible radio signals and lower investments in health. The most recent census in Benin, from 2002, has information on village population for nearly all of the villages in the sample. Poorer villages might have unobserved characteristics that affect both radio access and be targeted by the government for bed-nets distribution. Though there is no census or statistically representative data of incomes and economic 14
also indicate that in the long run, men with high levels of initial antisocial behavior who received a cash grant actually increase their anti-social acts. This is especially interesting given that the effects of cash on occupational choice and income disappeared after a year. One possibility is that this increase in antisocial behavior is a reaction to the failed attempt at legitimate livelihoods, but these results are more speculative than anything else.

# **E.6 Program impacts on occupational choice**

To measure changes in occupational choice, we asked respondents at each endline whether they had engaged in 22 occupations, from farming to petty business, trades, and formal jobs. For each occupation, we collected self-reported earnings and hours in both the last week and the week prior. We use these to calculate the total earnings and hours variables. With two endline surveys, we have four weeks of employment data per person both in the short-term and in the long term.

We can also calculate hours by occupations each week, aggregating our 22 occupations into 5 mutually exclusive categories:

1. Non-agricultural high-school work, which includes trading and office work

2. Non-agricultural low-skill business, which includes selling from a shop, selling at a table, buying and selling, engaging in petty trade, and conducting small business xlv
# 2 How big is the brain drain?

There is clear evidence that the brain drain has increased dramatically since the 1970s, both in absolute and relative terms. Nearly 30 years ago, the United Nations estimated the total number of highly-skilled South-North migrants for 1961-72 at only 300,000 (UNCTAD, 1975); less than a generation later, in 1990, the U.S. Census revealed that there were more than 2.5 million highly educated immigrants from developing countries residing in the U.S. alone, excluding people under the age of 25 (that is, without counting most foreign students). Country studies commissioned by the International Labor Organization also show that nearly 40% of the Philippines’ emigrants are college educated, and, more surprisingly, that Mexico in 1990 was the world’s third largest exporter of college-educated migrants (Lowell and Findlay, 2001). Since 1990, the chief causes of the brain drain have gained in strength and these increasing trends have been con...rmed. Indeed, selective immigration policies ...rst introduced in Australia and Canada in the 1980s have spread to other OECD countries: the Immigration Act of 1990 as well as the substantial relaxation of the quota for highly skilled professionals (H1-B visas) in the US constitute a very signi...cant change in the immigration policy of the country that remains the destination of about 40% of the immigrants to the OECD area. Finally, a growing number of EU countries (including France, Germany, Ireland and the UK) have recently introduced similar programs aiming at attracting a quali...ed workforce (e.g., through the creation of labor-shortage occupation lists) (OECD, 2002).

Until very recently, there were no comparative data on the magnitude of the brain drain. The ...rst serious e¤ort to put together harmonized international data on migration rates by education level is due to William Carrington and Enrica Detragiache from the International Monetary Fund, who used
so improve resilience and prevent longer-term erosion of productive capacity. Beyond informing design of such measures, the data presented here can potentially also be useful to assess their impact.

The rest of the paper is structured as follows. Section two provides background and motivation and introduces the strategy to estimate direct and indirect impacts of conflict. Section three describes data sources and highlights descriptive evidence. Section four presents econometric results for winter crop cover and NDVI of cereals and discusses their implications. Section five concludes by highlighting potential policy relevance and areas for future research.

# **2. Motivation and approach**

# **2.1 Conflict and food security in the literature**

A large literature identified weather or price shocks that affect households’ relative endowments as potential triggers for violent conflict by a large body of literature (see Berman _et al._ (2021) or Blair _et al._ (2021) for a discussion and references). Studies of the impact of violence on food security focus almost exclusively on the consumption side; in fact the review by Martin-Shields and Stojetz (2019) includes no studies that directly analyze conflict impacts on food production. Thus, while such studies highlight the need for proper counterfactuals and use of panel data, Ukraine’s situation is sufficiently different from that analyzed in most studies on conflict and food security to limit the transferability of substantive insights and require a different analytical approach.<sup>1</sup> One advantage of agriculture is that remotely sensed covers large areas consistently. Satellite imagery is now routinely used to consistently quantify changes in land use such as deforestation (Hansen _et al._ 2013) or crop expansion (Potapov _et al._ 2022) globally. In the US, a Landsat-based crop data layer is available since the 1980s to allow parcel-level analysis (Lark _et al._ 2017). For developing countries, the use of such imagery to provide reliable output or productivity indicators down to the plot level has recently been demonstrated (Lobell _et al.
towards offering tax breaks or subsidies to foreign investors, as such policies are not effective at attracting foreign investors or worthwhile.<sup>15</sup> Rather there should be an engagement in investment promotion efforts aiming at reducing the costs of FDI by providing information on business conditions and helping foreign investors deal with bureaucratic procedures (UNCTAD, 2018; World Bank, 2018). Thus, the role of investment promotion agencies in facilitating inward FDI can be vital.

> 15For instance, Haskel et al. (2007) examine within-sector productivity spillovers associated with FDI using UK data and conclude that it is quite easy to overpay when extending fiscal incentives to foreign investors.

14
our matched control group that might raise concerns about our findings.

We generalize this approach and consider the robustness of our results to control group composition more broadly. We do so by sequentially omitting all potential control RTAs from our matching approach and estimating the triple difference model in Equation (2). Our main findings are robust to this “leave-one-out” sensitivity exercise. We present results from this robustness exercise in the appendix where Figure A1 plots the distribution of the estimated total effects of RTA entry into force for agreements with and without environmental provisions ( _β_ 1 + _β_ 2 and _β_ 1 from Equation (2), respectively).

# **5 Country-level Analysis**

The matched sample analysis presented above accounts for the potential endogeneiety in RTA-level content, but our matching approach relies on the use of observable characteristics and uses relatively few unique RTAs without provisions as controls. We provide additional evidence supporting our main findings using an entirely different identification strategy based on a multiple overlapping event study using a country-level panel dataset. This approach allows for the inclusion of all countries and all RTAs enacted in our sample window and estimates differential dynamics around RTA enactment for agreements with and without relevant environmental provisions. We detail this approach below.

## **5.1 Multiple Event Study Framework**

We extend the event study framework in Abman and Lundberg (2020) to allow the dynamic effects of RTA enactment on net forest loss to differ by whether or not the RTA under study includes relevant environmental provisions with the following model: 22
this table. Additional detail on the construction of the indices can be found in in Appendix A and Appendix B. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to husbands of respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
While our results do not have enough power to statistically distinguish varying self/proxy discrepancies in child labor statistics over different agricultural seasons, the point estimates are ordered: self/proxy discrepancies in child labor reporting are at the lowest when the agricultural season involves routine steps and complementarity of tasks between children and adults i.e., planting and fertilizing usage, while higher discrepancies emerge in the lean (or hungry) season in which farm activities are less salient, income is scarce and, therefore, the heads of households might allocate effort to other off-farm activities to supplement income, or in the coffee harvest season, when farm labor demand is at its highest and farm activities can be done by children in their own. Future studies with greater power can potentially provide more definitive evidence on the seasonality of survey treatment effect for child labor statistics.

Our results are different from Dillon’s et al. (2012) experimental survey design on child labor statistics in Tanzania. We find that given variable and irregular work schedules across the agricultural calendar for girls, it is plausible to find higher self/proxy discrepancies in farm child labor statistics. The difference in results highlights the need to understand the type of activities children perform and the context in which the surveys are implemented.

Our results also offer information for survey costs and statistical power trade-offs. A detailed breakdown of survey costs showed that for potential interventions that target child labor as the outcome of interest, self-proxy reporting offers a good compromise between survey precision and survey costs mainly if the expected impact on child labor is small or moderate.

Overall, this survey design intervention in rural Ethiopia points out that studies of child labor in agricultural settings should explicitly acknowledge and discuss survey design gender gaps in child labor statistics for a better understanding of its determinants and conditions, and for the design of social protection programs and policies.

26
- <u>Corak, M., Lindquist, M. and B. Mazumder (2014).</u> “A comparison of upward and downward intergenerational mobility in Canada, Sweden and the United States.” _Labour Economics_ , 30: 185-200.

- <u>Cowell, F. (1985). “Measures of Distributional Change: An Axiomatic Approach.”</u> _Review of Economic Studies_ , 52(1): 135–51.

- <u>Durlauf, S., Johnson, P. and J. Temple. (2005). “Growth Econometrics,” Handbook of Economic</u> Growth, in: Philippe Aghion & Steven Durlauf (ed.), _Handbook of Economic Growth,_ edition 1, volume 1, chapter 8, pages 555-677, Elsevier.

- <u>EBRD (European Bank for Reconstruction and Development) (Various years).</u> _Life in Transition Survey_ . London.

- <u>Field, G., and E. Ok (1996). “The Meaning and Measurement of Income Mobility.”</u> _Journal of Economic Theory_ , 71(2): 349–77.

- <u>Foster, J., and M. Rothbaum (2023). “The Mobility Curve: Measuring the Welfare Effects of</u> Mobility.” Mimeo, Department of Economics, George Washington University, Washington, DC.

- <u>Ganzeboom, H., and P. Nieuwbeerta (1999). “Access to education in six Eastern European</u> countries between 1940 and 1985: Results of a cross-national survey.” _Communist and Post-Communist Studies_ , 32(4): 339–57.

- <u>Girdwood, S. and M. Leibbrandt. (2009). “Intergenerational Mobility: Analysis of the NIDS Wave</u> 1 Dataset”, National Income Dynamics Study Discussion Paper 15.

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. Hole-filled SRTM for the globe. Version 4, available from the CGIAR-CSI SRTM 90m Database. Retrieved from http://srtm.csi.cgiar.org

- Kleibergen, F; Paap, R (2006). Generalized Reduced Rank Tests using the Singular Value Decomposition. _Journal of Econometrics_ , 133(1): 97-126.

- Lee B; Gordon P (2007). Urban Spatial Structure and Economic Growth in US Metropolitan Areas. In 46th Annual Meetings of the Western Regional Science Association, at Newport Beach, CA. Accessed March 15, 2017.
>15</sup> They are especially important for the poor, who typically have higher numbers of unwanted children than the rich except in settings with very effective programs, such as Indonesia (Figure 5). Family planning programs also typically disseminate information on contraception, and on how lower fertility can help parents invest in their children and avail new opportunities for raising living standards.<sup>16</sup> Parents – especially poorer parents – have imperfect information on these issues. Households also appear to face difficulties in making optimal choices that involve long-term planning horizons (see for example Cronqvist and Thaler 2004 on pension decisions). Offering simple information on contraception ― or more complex messages through media such as soap operas that portray the lives of people with small families and how they access new opportunities ― helps reduce imperfect information. Many studies using cross-sectional survey data have found access to media significantly associated with increased contraceptive use and reduced fertility.<sup>17</sup> The few quasi-randomized evaluations of media effect have found it effective at altering fertility and contraceptive use in Tanzania (Rogers et al 1999) and reducing fertility in Brazil and India (La Ferrara et al 2008; Jensen and Oster 2009).<sup>18</sup> In a highly influential paper, Pritchett (1994a) argued that family planning programs have little impact on fertility: ―Ninety percent of the differences across countries in total fertility rates are accounted for solely by differences in women's reported desired fertility.... The results contradict theories that assert a large causal role for expansion of contraceptive use in the reductions of fertility.‖ Pritchett (1994: abstract) Many have taken Pritchett‘s study as indicating that effort on family planning programs is ill-spent, but he later concludes that his estimates imply that strengthening a family planning program substantially (by 50 points out of a scale of 0-100) would reduce fertility by one birth (Pritchett 1994b: 626). Bongaarts 13
**Figure 17. Ratio of Total Value‐added Exports (with Forward Linkages) to Gross Exports**

<!-- Start of picture text --> Services Manufacturing<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>1995 2000 2005 2010 1995 2000 2005 2010<br>Year Year<br>RUS CHN BRA RUS CHN BRA<br>IND ZAF IND ZAF<br>Total value added in exports/gross exports Total value added in exports/gross exports<br><!-- End of picture text --> Source: World Bank Trade Value Added Database and Francois et al., 2013. Note: RUS = Russia, CHN = China, BRA = Brazil, IND = India, ZAF = South Africa 41