rafmacalaba/data-use-mentions-extended huggingface dataset:
.5|
|_2000-2002_||||||
||Education|0.3|20.3|20.6|15.0|
||Age|-2.0|6.7|4.6|4.1|
||Economic Sector|-0.5|5.6|5.1|10.2|
||Status|0.6|17.8|18.4|14.4|

_Source:_ Calculations based on the ENEU (third quarter).

Third, the income effect is always prevalent. If one considers, for instance, the 1988–96 period, changes in the relative earnings among educational groups alone would have generated a larger deterioration in the earnings distribution than the one observed. The same holds true for the other periods. Even the decrease in inequality observed in recent periods is explained by the changes in relative earnings (the income profile related to education became less steep as shown in Table 2.1). Therefore, it seems reasonable to conclude that the income effect is the leading force behind the increase in inequality, and this, in turn, suggests that the workings of the labor market and its interaction with educational policies should be thoroughly examined.

Fourth, the significance of changes in the distribution of education remains high even when one controls for changes in other relevant variables. As a matter of fact, the marginal contribution of age, economic sector, and status in the labor market is negative in the 1988-1996 period. This means that changes in these variables reduced the effects induced by changes related to education, as most of the time they reduced inequality after 23
**Figure 1: Kernel density smoothing of propensity score across treatment and control samples**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>propensity score<br>Treatment Control<br>5<br>4<br>3<br>2<br>Kernel density<br>1<br>0<br><!-- End of picture text --> Table 2 shows the balance at baseline for the trimmed sub-sample across a range of the covariates. The sample at baseline is highly unbalanced before IPW, signaling the statistical challenge to adjust for these differences (Imbens 2015): beneficiaries live in larger households with more children and fewer working age adults, lower consumption per capita, and higher scores for negative coping strategies indices. After matching these differences are eliminated. A test of joint-orthogonality for the entire set of covariates shown in the balance table returns a p-value of 0.897. Baseline balance in the matched sample is maintained across survey waves, despite high rates of attrition (Appendix Table 3).<sup>20</sup> Since causal identification of program effects hinges on conditional independence, it is useful to consider how similar households ended up with different beneficiary status at baseline. As described in Section 3, the study sample was randomly drawn from the pool of ESSN applicants, all of whom submitted applications between November, 2016 and May, 2017. Upon the receipt of the application, the information declared by the household is cross-checked and verified digitally through the databases integrated in the ISAS system. While this system was best practice and hard to game, there could be discrepancies between a household’s demographic characteristics at baseline and what was registered in the underlying databases (PDMM, MERNIS). As described in detail in Section 2, such discrepancies could exist due to the absence of documentation, spelling differences in existing documents, a backlog of cases to be processed, or difficulties
**Table 1 Studies on capital formation in China**

|**Author**|**Year of**<br>**Publication**|**Methodology**|
|---|---|---|
|Liu and Yeh|1963|Based on capital stock data for 1955 published by the State<br>Statistical Bureau in 1957, back-cast to 1952 using investment data.|
|Chow|1993|Based on data for 1952 of the original value of the fixed capital stock<br>of state-owned units published in the Statistical Yearbook.|
|Wu|2016|Partially based on the 1951 census of national assets|
|Baillu, Kruger_et al_|2016|Based on growth rate of investment and depreciation rate|
|Maddison|2007|Based on his own estimates of investment in the pre-revolutionary<br>period.|
|Perkins and Rawski|2008|Capital-output ratio set at an assumed level|
|Perkins|1998|Capital stock set a multiple of net material product in 1952|
|Wu Yarwi|2016|Assumes capital stock of zero in 1900, uses an estimated series for<br>investment between 1900 and 1952 to generate a capital stock in<br>1952.|
|Jun_et al_|2004|Based on growth rate of investment and assumed depreciation rate|
|Feenstra_et al_|2016|Based on cross-country analysis for Penn World Tables|

**Table 2 Estimates of the capital stock in 1952**

|Author|Capital Stock<br>current prices<br>1952|Capital-<br>output<br>ratio 1952|Comments|
|---|---|---|---|
|**_Studies that exclude housing_**||||
|Liu and Yeh|55.9|0.7|Excludes government|
|Chow|76.6|0.9|Excludes government|
|Wu|160.0
earnings functions. These were estimated with data from two income-expenditure surveys (ENIGH) carried out by the National Statistical Institute (INEGI) in the third quarters of 1984 and 1994. These two survey years provide an excellent setting for comparisons: first, both years can be considered macroeconomically comparable in that the economy is neither in recession nor booming. Secondly, both years are almost equidistant in time from the major stabilization and structural reform measures implemented
endowment of technological abilities and investments in upgrading that ability (as proxied e.g., by the level of R&D spending).

The paper is organized as follows. Section II briefly reviews the relevant literature on channels of technology transfer and spillovers. Section III describes the data set. Section IV lays out the estimation approach. Section V presents the results of the empirical analysis. Section VI concludes.

# **II. Channels of Technology Transfer**

While there is little doubt that technologies make their way across international boundaries, the mechanisms through which this occurs are not well understood. Aside from case studies, most of the empirical evidence is based on aggregate data or cross sectional surveys, and is subject to multiple interpretations. Various transmission channels may play a role in the technology transfer process. New technologies may be embodied in goods and transferred through imports of new varieties of differentiated products or capital goods and equipment, or through anus-length trade in intellectual property, e.g., licensing contracts. Firms may learn about technologies by exporting to knowledgeable buyers, who share product designs and production techniques with them. Technology transfer will also occur in the context of formal cooperative arrangements between foreign and local firms, e.g., FDI (acquisition) or project-specific joint ventures.<sup>2</sup> In all these cases technology acquisition will require the availability of workers with appropriate training and expertise to allow technology absorption and adaptation. The absence of such a

> **2** See e.g., Helleiner (1973) and Keesing and Lall (1992) on sub-contracting; Feenstra et al. (1992) on imports of inputs; Blomstrom and Kokko (1997) for a recent survey of the literature on FDI; and Pack and Saggi (1997) for a general survey of the literature on technology transfer.

4
classify these occupations properly, I define a category ‘non-construction’ that includes all jobs that are definitely not in construction sector. The remaining jobs are either in construction or in other sectors that are not classifiable. As Table 2 shows, only a third of the workers identified going to work that can be classified as not being in the construction sector and there is a large variation across destinations (column 6).

Furthermore, the reported wages and fees from this source differ from the expectations of potential workers. In Shrestha (2017), I find that potential work migrants expect to earn much higher while abroad and pay much higher fees to migrate to these destinations. The discrepancy results from misinformation in part of the potential migrants as well as misreporting from migrants to DoFE. Migrants may misreport their wages and fees to DoFE as the government has regulations on minimum wage and maximum fees for workers seeking employment abroad. Furthermore, the wages reported to DoFE do not include overtime work, which can be a large part of migrant earnings abroad. In any case, the wages reported here _are_ contractual wages and potential migrants have to submit a copy of the contract from the employer for their application to the DoFE.

# **2.3 Data on migrant deaths**

The data on migrant deaths comes from the Foreign Employment Promotion Board (FEPB). FEPB is a government body established to make foreign migration safe and organized. One of its main tasks include providing financial support to the family of the deceased and helping them retrieve bodies of the deceased workers. When a registered (received labor permits from DoFE) migrant worker dies abroad, his family is eligible to receive a compensation (of over $1,500 for the study period) through the FEPB. The FEPB provides compensation for any kind of death as long as the migrant had obtained permits from DoFE, died within the duration of his contract, and the family files a claim within a year of the date of death with necessary documents. The necessary documents
Europe is an ideal setting for this study. Countries in Europe enacted dozens of education reforms in the twentieth century, expanding the number of years of education legally mandated through compulsory schooling laws. At the same time, Europe has large, harmonized multi-country surveys, enabling credible within- and cross- country analyses, with recent climate modules added to the European Social Survey (ESS), which we analyze in this study. Moreover, Europe has a robust green party movement, which has an explicit environmental agenda.<sup>3</sup> We codify a novel dataset of green party voting outcomes, enabling identification of pro-climate voting behavior.

Our analysis focuses on outcome indices as well as on specific indicators within each index, including comparisons between correlations and causal estimates. We find significant impacts on nearly all pro-climate measures. Our headline results show that an additional year of education leads to an increase of 4.0 percentage points (PP) in pro-climate beliefs, 5.8 PP in behaviors, 1.0 PP in policy preferences, and 3.6 PP in green voting. Relative to status quo rates, these impacts are non-trivial, translating into 6.3% increase for beliefs, 8.5% for behaviors, 1.7% for policy preferences, and a striking 35.0% increase for green party voting.

These results are notable since education has been conspicuously absent from most major climate change discussions.<sup>4</sup> Our findings suggest expanding general education should be added to the menu of approaches considered in tackling one of the greatest modern threats to human well-being. Indeed, human capital accumulation may be vital in shaping beliefs about the costs and benefits of policies to reduce emissions (Dechezleprˆetre et al., 2022) and extend directly to consequential outcomes such as policy preferences and voting.

The rest of the paper is organized as follows. The next section describes our data. Section III details our empirical strategy and Section IV presents our results. Some brief concluding
deflators). See the Appendices for more information on the construction of our data and key explanatory variables.

# **5. A Bird’s Eye View of Reallocation: Job Flows and Aggregate Productivity Dynamics**

# **5.1  Job Flows, Entry and Exit**

Table 1 and Figure 2 present aggregate entry, exit and employment growth statistics. Average exit over the entire period is 8.8% while average entry is 11.1%. Firm exit spiked during the crisis - in 1997 10.8% of firms exited, while in 1998, 11.2% of all firms exited - and dropped precipitously during the recovery in 1999 and 2000, to peak again in 2001 (see Appendix A4). Employment growth followed a similar trend, but did not spike in 2001; before the crisis, manufacturing employment grew quite rapidly. The crises induced substantial job losses; on average firms shrank employment by 1.4% in 1997 and by 3.7% in 1998. Employment growth recovered in 1999 and 2000, but dropped in 2001.

The high job losses during the crisis were driven both by a slowdown in job creation and by a spike in job destruction, and predominantly accounted for by employment adjustment by incumbents (see figure 2). The share of job flows accounted for by firm entry and exit is likely to be underestimated, however, since we only observe firms with at least 20 employees. The amount of gross reallocation far exceeded the amount required to achieve net employment adjustment, leading to enormous excess churning. The increase in excess churning attests to the importance of heterogeneity across firms: even during the crisis, almost a third of all firms reported expanding employment. Nevertheless, a striking feature of the data is how persistent employment is; over the entire period considered on average 21% of firms did not change their labor input each year. The figure also shows a longer trend towards lower job creation, both in aggregate and in net creation rates.
from road map (own<br>calculation)|Bird distance to the nearest road calculated for each pixel of the 100 meter rasterized<br>road map.|Map 10|
|Agricultural suitability of<br>Mato Grosso|1995|2002|1; 250,000|State Secretary of Planing and<br>Coordination (SEPLAN)|Agricultural suitability was defined by SEPLAN as part of the socio-economic ecological<br>zoning exercise (ZSEE) in Mato Grosso.|Map 11|
|LandvalueofMatoGrosso|2003|2003|1:250,000|derivedfromINCRA2003|Geo-referenciationoflandvaluestomunicipalboundariesofMatoGrosso|-|

<!-- Start of picture text --> Natural vegetation cover of 1995 = 1; 250,000 Derived from land use/ Natural vegetation cover corresponds to the land cover (without any human activity as of Map 7<br>Mato Grosso land cover map (own calculation) 1995) of the SEPLAN land use/land cover map.<br>High/low natural vegetation 1995 = 1; 250,000 Derived from natural vegetation Mean natural vegetation cover on a 25x25 pixel neighborhood (corresponding to a mean Map 8<br>cover in Mato Grosso cover map (own calculation) property size in Mato Grosso of 625 hectareas) was calculated for each pixel on the 100<br>meter rasterized natural vegetation cover 1995. The resulting mean values were ove<br>Elevation of Mato Grosso 2002-2003 2003 90 meter US Geological Survey (USGS) Digital elevation model for Mato Grosso from the Shuttle Radar Topography Mission (SRTM) =<br>Slope of Mato Grosso 2002-2003 o 90 meter Derived from elevation grid (own Slope was calculated from elevation grid. Map 9<br>calculation)<br>Roads of Mato Grosso unknown 2003 1; 250,000 Mato Grosso State Foundation of Primary (paved) and secondary (unpaved) roads in Mato Grosso =<br>the Environmental (FEMA)<br>Distance to roads in Mato unknown = 1: 250,
a direct link the the tax form, and was personalized with the name of the addressee featuring in the address eld. The message also presented the social norm8 out of 10 [tax-registered] Costa Ricans have led their 2014 income tax returnand introduced the possibility of public shaming through the publication of names. The social norm and the deterrence content of the message are all fact-based and credible, given the usual enforcement activities the tax authority conducts through audits, rm closures, and the online publication of the list of late-lers (for the sales tax) and late-payers (for various taxes).<sup>21</sup> As the tax authority routinely uses third-party information in its enforcement activities and mentions this to taxpayers, the baseline treatment message (treatment 1) also stated that We have third-party information, conrming that you or your client performed activities in 2014 which require you to pay taxes.<sup>22</sup> Treatment 2 further strengthened this statement by providing rms with specic examples of third-party information held by the tax authority about their activities:

# Treatment 2 (T2):

We have third-party information conrming that you or your client performed activities in 2014 which require you to pay taxes. From third-party reports (D150, D151 and D153), we know about your operations, for example:

- Sales of at least XXX reported by COMPANY,

- Sales from credit/debit cards of at least ZZZ reported by BANK,

- Sales or contracts with state entities of at least WWW.

For each type of third-party information held by the tax authority (reports from other rms, state institutions, and credit/debit card companies), it listed the transaction amount and purchaser of the highest reported transaction. Providing evidence of third-party reported transactions and transaction partners clearly demonstrates the tax authority's possession of third-party reports and thus makes the message highly credible. In addition, providing examples rather than the total amount of third-party reported transactions,
areas. Koubi et al. (2022) use survey data from Cambodia, Nicaragua, Peru, Uganda, and Vietnam and document that less educated and lowerincome people are less likely to migrate after exposure to fast-onset shocks, compared to people endowed with higher education and more economic resources. Finally, even in the absence of sudden resource depletion, in poor countries the populations affected may not have enough monetary resources for longdistance migration (Zickgraf and Perrin, 2016). Cattaneo et al. (2019) emphasize that the main current insight of this strand of the literature is that the potential for fast-onset events to cause long-term, longdistance migrations appears limited, especially in the case of costlier international migration.

In contrast, slow-onset changes are more likely to induce migration than rapid-onset ones, but the

> 4 These five thematic subsections have been selected among many topics, due to their relevance and prominence for the assessment of data gaps we conduct.

5
51 The questionnaire asked about the main cooking fuel and about the expenditure on LPG during the previous month. LPG was by far the most commonly used fuel, followed by firewood and natural gas (Figure A2.5). As expected, virtually no households purchased both LPG and natural gas.

Figure A2.5: Main source of energy for cooking across all households in Mexico <!-- Start of picture text --> LPG 80<br>firewood 12<br>natural gas 6.4<br>electricity 0.5<br>coal/charcoal 0.2<br>other 0.4<br>0 20 40 60 80 100<br>Percentage of households<br><!-- End of picture text --> _Source:_ Authors’ calculations.

LPG is sold in 10-, 15,- 20-, 30- and 45-kg cylinders to residential consumers, with the last three being the most common sizes. Cylinder sales accounted for about half of LPG sold to residential consumers in 2007 (Mexico, Ministry of Energy 2008). Instead of cylinders, many middle- and high-income households have large tanks—typically 300 liters in capacity (about 165 kg)— installed on rooftops, which are refilled by LPG delivery trucks. Apartment complexes can also have large tanks serving multiple households. Prices are independent of cylinder and tank sizes.

The relatively large sizes of cylinders and tanks commonly used in Mexico appear to have resulted in many LPG-using households reporting zero expenditure in the previous month: 80 percent of households reported that LPG was their primary cooking fuel, but only 60 percent had purchased it in the previous month. The 4.8 million households that cited LPG as their main cooking fuel but had not purchased it presumably represents those who had bought large quantities of LPG but not in the month preceding the survey. Because of this large discrepancy, data on LPG as the primary cooking fuel are used in the selection equation. The quantities of
“smoker” if they report positive expenses in tobacco products. Conditional shares are calculated only for households reporting positive expenditures on each type of cigarettes.

# VIII. Incidence results

Each component of Equation (1) of the ECBA was simulated using data from the IHS 2016, assuming three policy scenarios: 25 percent, 50 percent and 100 percent price increase on all cigarettes. The calculations below also assume that changes in household welfare related to smoking-attributable expenses only affect welfare through out-of-pocket payments.<sup>17</sup> Alternative simulations incorporating the value of total smoking-attributable expenses in the country—as well as imputing other price-elasticity structures—are available in Annex 7. All simulations reflect annual impacts for 2017.

## (A) Change in tobacco expenditures

Increasing the prices on tobacco products while holding all other things equal, negatively affects all smoker households, as they are forced to allocate a higher share of their budgets to continue to purchase the same quantities of tobacco. This scenario is known as “direct pass-through” (solid line on <u>Figures 3, Panel A to C),</u> as there are no changes in consumption, and the price-shock on tobacco is passed-though to or absorbed by smokers entirely. Because tobacco purchases represent a higher share of household budgets for lowerincome smokers, the direct pass-through effect is regressive, exercising higher damage to poorer smokers.

Nonetheless, households adjust consumption when faced with higher prices. As explained above, it is the interaction of the price shock, the price-responsiveness of each group of the population—incorporated as price elasticities—and the share of tobacco in household budgets that determine the change in tobacco expenditures. <u>Figure 3</u> presents these results with dotted lines for different elasticity assumptions. Assuming a 25 or 50 percent price increase (Panels A and B), the effect is still generally negative and somewhat regressive. Though the magnitude and regressive pattern is more moderate than under direct

> 17 The national estimate of smoking-attributable medical expenses
The indicators that were extracted from Cargo iQ are based on a pair of milestones: NFD and DLV, i.e., timestamp of the notification of document’s readiness and final handing over of the physical shipment to the freight forwarder. These milestones have the highest compliance levels. The time elapsed between events NFD and DLV was computed for each e-AWB recorded in the system at destination country given the validity of the time difference (both timestamps have to exist and the time difference between them has to be positive). Destination country aggregates presented as part of the aviation pillar of natural unit indicators were constructed from these time differences derived for each e-AWB. Country-level aggregates computed were means, medians, and absolute square deviations.

Figure 3 shows the country coverage of the final subset of the Cargo iQ dataset. As was the case with the survey-based LPI, low-income countries have the least coverage (only a quarter of LICs are available as part of aviation statistics). Geographical coverage is lower overall for Cargo iQ in comparison with UPU. East Asia & Pacific, South Asia and Sub-Saharan Africa all have about 35-40 percent of countries covered. Overall, slightly less than a half of the World Bank member countries have records sourced from Cargo iQ information on aviation supply chains and logistics. For a more detailed overview of Cargo iQ data, refer to Appendix 3.

_Figure 3: Country coverage of Cargo iQ dataset by WB region and income group_ Figure 3 Cargo IQ country coverage <!-- Start of picture text --> 66.7%<br>58.6% 57.1%<br>60%<br>41.5%<br>39.5%<br>37.5%<br>40% 35.4%<br>20%<br>0%<br>East Asia & Pacific Europe & Central Latin America & Middle East & North America South Asia Sub-Saharan Africa<br>Asia Caribbean North Africa<br>80%