rafmacalaba/datause-encoder-data huggingface dataset:
observed. Section V studies the contribution of these flows to the changes in labor force shares identified in section IV and discusses the implications of the theoretical analysis of labor markets in developing countries. Section VI concludes.

# II. Data

## _Mexico_

The data for Mexico are drawn from the National Urban Employment Survey (Encuesta Nacional the Empleo Urbano, ENEU) that conducts extensive quarterly household interviews in the 16 major metropolitan areas. The questionnaire is extensive in its coverage of participation in the labor market, wages, hours worked, etc. that are traditionally found in such employment surveys. It is structured so as to track a fifth of each sample across a five quarter period. We have concatenated panels from the first quarter of 1987 to the fourth quarter of 2004. Each individual contributed with four transition pairs. In constructing each quarterly pair, we lose roughly 10% of the sample to attrition. Antman and Mckenzie (2007) conclude after careful analysis of the same data that the bias resulting from this attrition is likely to be very small.

The ENEU has suffered only minor modifications during the covered period but it has substantially changed its geographical coverage. From 1987 to 1992 the survey comprised 16 major urban areas. In 1992, 18 more urban areas were introduced and throughout the following years additional cities were included in the sample to reach 44 at the beginning of 1998. We choose to constraint our sample to the original 16 cities although all results are similar with extended the sample.

## _Brazil_

The data for Brazil are draw from Monthly Employment Survey (Pesquisa Mensal de Emprego, PME) that conducts monthly household interviews in 6 of the major metropolitan regions (covering 25% of the national labor market). The questionnaire is very similar to the ENEU, although some important differences are discussed below. The PME is structured so as to track each household during four consecutive months and then drop them from the sample for 8 months, after which they are reintroduced for another 4 months. The rotation procedure is such that each month one fourth of the sample is substituted by households to form a new panel. Thus, after 4 6
Table 5: Impact by skill group

|Dep. var. ln(postings+1)|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|||Pa|nel A: Low s|kill||Baseline|
|Output tariff exposure|-0.052***<br>(0.016)|||-0.006<br>(0.016)|-0.007<br>(0.016)|-0.006<br>(0.015)|
|Imported input tariff exposure||-0.024***<br>(0.007)||-0.022***<br>(0.007)|-0.022***<br>(0.007)|-0.020***<br>(0.007)|
|Export tariff exposure|||-0.152***<br>(0.046)|-0.140***<br>(0.046)|-0.133**<br>(0.049)|-0.120***<br>(0.036)|
|Total ag subsidy|||||0.001<br>(0.001)|0.001*<br>(0.001)|
|Ag subsidy * Export tariff exposure|||||-0.000<br>(0.000)|-0.000<br>(0.000)|
|Adjusted R-squared|0.964|0.964|0.964|0.964|0.964|0.977|
|||Pan|el B: High s|kill||Baseline|
|Output tariff exposure|-0.038**|||-0.008|-0.010|-0.006|
||(0.016)|||(0.017)|(0.016)|(0.015)|
|Imported input tariff exposure||-0.016**<br>(0.006)||-0.014**<br>(0.006)|-0.015**<br>(0.006)|-0.020***<br>(0.007)|
|Export tariff exposure|||-0.110***|-0.102***|-0.099***|-0.120***|
||||(0.033)|(0.032)|(0.034)|(0.036)|
|Total ag subsidy|||||0.001**<br>(0.001)|0.001*<br>(0.001)|
|Ag subsidy * Export tariff exposure|||||-0.000<br>(0.000)|-0.000<br>(0.000)|
|Adjusted R-squared|0.964|0.964|0.964|0.964|0.964|0.977|
|Observations|14,640|14,640|14,640|14,640|14,640|14,640|
|FE|CZ YM|CZ YM|CZ YM|CZ YM|CZ YM|CZ YM|
|Cluster|CZ YM|CZ YM|CZ YM|CZ YM|CZ YM|CZ YM|

Notes: This table displays the results from the regressions of the log of monthly job postings in each commuting zone on the associated trade barrier exposure measures, with the addition of controls for agricultural subsidies. The postings variables are broken down into high and low skill postings using the average education level (threshold of 14 years of education) of their associated 2-digit SOC occupations. All specifications include CZ and month-year (YM) fixed effects, and standard errors (in parentheses) are two-way clustered at the CZ and month-year level. Tariff exposure measures should be interpreted in units of $1,000 dollars per worker and agricultural subsidies in units of $1,000,000. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

30
- 8 -

**Figure 3**

<!-- Start of picture text --> Commission Rates (% of the Premium), 1990-2004<br>7%<br>6%<br>5%<br>4%<br>3%<br>2%<br>1%<br>1990 1992 1994 1996 1998 2000 2002 2004<br><!-- End of picture text --> <!-- Start of picture text --> Source: SVS<br><!-- End of picture text -->

# **3. An Heuristic Model of the Annuity Rate**

The annuity rate is determined by the interaction of the flow demand and flow supply of annuities. The theoretical derivation of the flow demand and supply of annuities would require solving models of inter-temporal maximization for consumers and providers and is beyond the scope of this paper. Instead, this section follows an heuristic approach, examining the most important determinants of the annuity rate based on the factors that may affect the flow demand and supply of this product, both at the aggregate and the company levels.

The aggregate flow demand for annuities in any given period of time (e.g., a year) is determined by five major groups of factors: (i) the retirement rules, combined with the demographic structure of the working population; (ii) the history of contributions and the return performance of pension funds during the accumulation phase; (iii) the menu of retirement instruments, including lump-sums; (iv) the rate of return on annuities vis-à-vis alternative retirement instruments; (v) the risk profile and preferences of retiring workers. Each of these major groups of determinants is briefly examined below.

Retirement rules and the demographic structure of the working population together define the number of workers eligible for retirement in any given year, and the universe of potential annuitants in that year. For example, an increase in the regulated normal retirement age, or more stringent conditions for early retirement produce a decline in the number of retiring workers and a resulting contraction in the flow demand for all retirement products, including annuities.

The historic performance of pension funds (and the history of contributions) also affects the demand for annuities because it defines the size of the pension balance for any given cohort. For example, a prolonged and sustained period of higher returns results in larger pension balances, possibly leading several cohorts to anticipate retirement and increase the demand for all retirement products, including annuities. By contrast, negative returns on pension savings in the years preceding retirement may lead several cohorts to postpone retirement in order to achieve their target retirement income. A sharp drop in returns may also cause many retirees to fail to meet legal criteria for early retirement.
Some firms chose to reapply with a different (lower) number of seats requested, while others did not. The process between a course request and the first day of a course can vary, and is discussed in further detail below.

MDIC provided us a comprehensive listing of the course requests in 2014, which contains a measure of the demand for courses/trainees by requesting firms. Each of these requests includes the number of people the requesting organization would like trained (seats), as well as the name of the company or organization that submitted the request, the company’s tax ID, the course requested, the occupation code related to the course, and the municipality in which the course is requested.

Table 1 shows that there were 16,782 records for course applications to MDIC in 2014. Of these, approximately half (8,340) were approved by MDIC. The average number of seats requested in a given course was approximately 38; the average number of seats in approved courses was only slightly higher at 43.6. Not all requestors were firms, however: 17% of course requests were made either by industry or workers’ associations. Among the firm requestors, we are able to match 97% to administrative employment records (described below) based on either the tax ID or the combination of firm name and municipality.

Requests approved by MDIC were then forwarded to the Ministry of Education, which is the body responsible for the overall administration of _Pronatec_ . The Ministry of Education aggregates course demands across ministries, and this aggregation is further screened according to course viability (i.e., having the minimum number of seats to hold a course in a municipality requested across ministries), technical criteria, and budget availability. The Ministry of Education is also responsible for selecting training providers and for setting the criteria for the registration of students. There are a number of providers that offer _Pronatec_ courses, although the majority are offered by Brazil’s _Sistema S_ , which in principle ensures projected needs. If found excessive, course requests would be denied (as opposed to adjusted) to discourage firms from seeking training that significantly exceeded their projected needs.

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9 a comparative advantage in environmentally sensitive industries, which was increased/maintained over the period. Sweden maintained its comparative advantage in the sensitive industries while losing markets in total and manufactured goods. The US has never had a comparative advantage in these products. The index for the US and Germany remained stable. The sensitive industries did better in world markets than total or manufactured exports in these countries. Japan has clearly lost its comparative advantage in environmentally sensitive goods, with its index falling from 1.2 to 0.6 during the period. The larger relative decline in the RCA compared to its market share suggests that the increase of other exports in Japan is responsible for much of the decline. Norway is still competitive, but has lost markets since 1970. For Norway this is likely to reflect the increase in oil revenues in total exports (denominator).

**Table** **_5:_ Revealed comparative advantage indexes in environmentally sensitive goods, 1970-90**

|Regions/Countries|1970|1990|
|---|---|---|
|**Industrial**|1.1|1.0|
|**Austria**|1.3|1.4|
|Fuiland|2.7<br>|2.9<br>|
|Norway|2.3|1.5|
|Sweden|1.7|1.7|
|crrmany<br>|**1.0**|1.0|
|Japan|**1.2**|0.6|
|Us|0.8|0.8|
|Developing|0.8|0.9|

Souirce: Derived from United Nations COMTRADE database. World exports based on world import data.

At the product level there are gains and losses in comparative advantage. This, in itself, suggests that factors other than environmental standards have been more important for trade performance in environmentally sensitive goods. As the goods in the sample are assumed to have been subject to high environmental standards, if these were to have an effect on trade performance one would expect a more uniform pattern. It is notable that in many countries certain sensitive industries lost competitiveness, while others gained it (Table 6).
# **Table C.3: Second respondents identified in pre-survey**

**Panel A: Promotion analysis (Wave 1)**

||N|Percent|
|---|---|---|
|Husband|160|69_._57|
|Father|11|4_._78|
|Brother|4|1_._74|
|Mother|4|1_._74|
|Sister|5|2_._17|
|Brother-in-law|1|0_._43|
|No other adult|45|19_._57|
|Total|230|100_._00|

**Panel B: Role model analysis**

||N|Percent|
|---|---|---|
|Husband|453|72_._95|
|Father|22|3_._54|
|Brother|6|0_._97|
|Mother|19|3_._06|
|Sister|9|1_._45|
|Other from<br>your family|1|0_._16|
|Father-in-law|1|0_._16|
|Mother-in-law|1|0_._16|
|Sister-in-law|2|0_._32|
|No other adult|107|17_._23|
|Total|621|100_._00|

**Table C.4: Variable definitions**

|Variable|Defnition|
|---|---|
|Supervisor (Dummy)|**Panel A: Outcome variables in main text**<br>Respondent reports working in a supervisory position in the gar-<br>ment industry. This includes the following positions: Quality Man-<br>ager, Assistant Line Supervisor, Line Supervisor, Line chief/Floow-<br>in-charge, Assistant production manager, Production Manager, IE<br>Manager|

55
||(0.055)|(0.020)|(0.067)|(0.095)|(0.040)|(0.023)|
|---|---|---|---|---|---|---|
|... FTA|0.070|0.045*|-0.043|0.171|0.049|0.056*|
||(0.066)|(0.025)|(0.079)|(0.116)|(0.045)|(0.030)|
|... lag( ln RER t-(t-1))|0.166***|0.119***|0.199***|0.029|0.111***|0.123***|
||(0.051)|(0.022)|(0.057)|(0.115)|(0.042)|(0.026)|
|Observations|424,452|3,086,669|314,506|109,944|901,307|2,185,362|
|R-squared|0.864|0.821|0.873|0.821|0.830|0.818|

Note: Robust standard errors clustered at the cluster exporter-importer-product level are in parentheses. All specifications include importer-exporter-product, importer-year, and exporter-year fixed effects. The samples include the top 15 producers of automobile or top 15 exporters of final electronics other than Japan. *** p<0.01, ** p<0.05, * p<0.1 Table 5 explores the effect of additional country characteristics on exports. There is no evidence that firms shun countries at a higher risk of natural disasters. Production did not relocate to previous top exporters. Contiguity to the export market appears to be a very important determinant of final auto exports (column 4), which may be due to the high transport costs of final autos. For auto parts and electronics, there is however no evidence that supply chains were increasingly regionalized. The presence of a Free Trade Area (FTA) appears to matter for final electronics. As tariffs tend to be higher on final than on intermediate imports, firms might seek to relocate final production in FTA members because of tariff escalation. A depreciating exchange rate in the preceding period may have also served to attract production in electronics and auto parts. While auto production relocated to countries with a revealed comparative advantage in the years preceding the shock, this was not the case for electronics. It is possible that these countries had a latent comparative advantage in electronics, but the presence of fixed costs of building a factory created inertia that prevented production relocation (Section 2). The earthquake, with its physical destruction and the need to rebuild, thus allowed these countries’ comparative advantage to emerge.

# **5. Conclusion**

This paper investigates the long-term consequences of natural disasters on the reconfiguration of global value chains. Focusing on the 2011 earthquake off the Pacific coast of Tōhoku in Japan, the largest ever recorded, we study how the shock affected trade in automobiles and electronics –two sectors that rely heavily on Japanese suppliers.

We find that countries more dependent on Japanese suppliers before the shock experienced larger declines in imports from Japan. But the decrease for intermediate auto and electronics was less 19
# TABLE 8S nOW OF FUNDS FOR COMPOSITE COUNTRIES

|XCGNP|||**Com**|**posite**of|**six**cou|ntries||||
|---|---|---|---|---|---|---|---|---|---|
|||Househol|d||Business||Genera|l Gover|nment|
||Not|Ass|Liab|Net|**Ass**|**Liab**|Net|Ass|Llab|
|Currency and deposits|6.1|6.1||2.4|2.4|0.1|0.8|0.9|0.1|
|Securitles|1.1|1.2|0.1|-1.8|0.5|2.3|-0.8|0.1|0.9|
|EquLtles and stocks|1.8|1.8||-2.5|1.0|3.5|1.0|1.0||
|Loans|-3.6||3.6|-8.9|1.4|10.3|-3.1|1.1|4.2|
|Insurance and pension|1.6|1.6||||||||
|Trade credit|-0.7|-0.1|0.6|0.6|2.3|1.7||||
|Other|0.5|0.3|-0.2|-1.3|0.6|1.9|2.1|2.2|0.1|
|Total|6.7|10.9|4.2|-11.5|8.3|19.8|0.1|5.4|5.3|
|S GNP||Compo|site ofei|ght<br>count|rLes|||||
|||Househol|d||Buslness|||||
||Net|Ass|LLab|Net|Ass|LLb||||
|Currency<br>anddeposits|9.3|9.3||2.8|2.9|0.2||||
|SeouritLes|1.1|1.2|0.1|-1.2|0.6|1.8||||
|Equities<br>and stocks|1.0|1.0||-2.1|0.8|2.8||||
|Loans|-3.7|0.0|3.7|-11.8|2.3|14.1||||
|Insurance<br>andpension|1.2|1.2||||||||
|Trade<br>credit|-0.6|0.0|0.5|0.3|5.3|5.0||||
|Other|0.4|0.2|-0.1|1.6|3.3|1.7||||
|Total|8.7|12.9|4.2|-11.9|15.3|27.2||||

Cameroon, Cote d'Ivoir-e Ecuador, Korea, Portug-l, Thailand, Turkey and Yu6osLavia Figures for goverr.mnt sector not avaLlable for Portugal or Yugoslavia.

52
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" a yard tap, rather than an in-house tap. The in-house tap variable reported in Figure 1 thus understates the number of households with private water connections. It is only possible to identify households with yard taps in seven of the fifteen countries in this sample. In those seven countries, almost none of the poorest households had sewer connections or in-house connections and yard taps, but at higher income levels in-house connections and yard taps were much more prevalent than sewer connections (Figure 2).

Second, information on sewer connections is only available for twelve of the fifteen countries. When households in only those twelve countries are pooled and divided into quantiles of 5 percent, it becomes clear that sewer coverage lags behind coverage of in-house water taps as expected (Figure 3). Sewer coverage is consistently about 10 percent lower than in-house water tap coverage for households with incomes (as approximated by the consumption aggregate) under US$400 per month; above US$400 per month, the gap between in-house water service and sewer connections actually widens. As in Figure 1 electricity coverage is higher than coverage of other infrastructure services at all income levels. In the remainder of the paper we present results for the pooled sample of households from all fifteen countries (as in Figure 1), and, except where noted, we use coverage figures for in-house water taps (rather than in-house connections and yard taps).'<sup>5</sup> 3.2. Coverage in urban and rural areas As anticipated, a smaller percentage of rural than urban residents had infrastructure services in their homes.<sup>16</sup> Fewer rural households had electricity (46 percent vs. 89 percent in cities), in-house water taps (12 percent vs. 59 percent), sewer connections (7 percent vs. 61 percent), and telephones (8 percent vs. 3 8 percent). The poor live disproportionately in rural areas, but urban/rural location does not alone explain the urban/rural infrastructure gap.'<sup>7</sup> Figures 4 and 5 show that a smaller percentage

> 15 As a result, the coverage differences between sewer and water service and the important role of yard taps in water service coverage, both of which are apparent in Figures 2 and 3, will be obscured.

> 16 The urban/rural classifications made by LSMS survey teams have been adopted for this analysis.

> 17 More than 91 percent of households in the poorest quantile of the pooled sample live in rural areas, whereas only 13 percent of the richest households are rural residents.

8
relative to high carbon fuel consumption as carbon emission permits change. This reflects the very limited reductions in high carbon fuel consumption in response to carbon permit prices during the first phase of the EU ETS. Overall, the Morishma elasticities reflect very limited price-induced substitution between alternative generation fuels in the production of electricity in the short-run.

# **7. Conclusions**

This study provides an analysis of the underlying economic forces inducing adjustments in electricity production factor intensities during the first phase of the European Union’s Emissions Trading System regulating emissions of greenhouse gas emissions. Our empirical analysis examines the demand for carbon permits, carbon based fuels, and carbon-free energy for 12 European countries using monthly data on fuel use, prices, and electricity generation. Our empirical model is unique because it considers all possible sources of generation within one model. Heretofore, empirical models of factor substitution in electric power generation were confined to studies of steam power generation using combustible fuels apart from nuclear of hydroelectric generation because prices for the latter fuels are not observable. Our approach uses a restricted variable cost function treating these factors as quasi-fixed to estimate the shadow value of these resources.

Our results suggest several conclusions. Perhaps the most important finding is that very limited substitution possibilities in combination with low carbon permit prices may explain the limited success of the EU ETS in achieving carbon emission reductions in the electric power generation sector. While our empirical results demonstrate that switching to nuclear and renewable energy is induced by higher carbon permit prices, the extent of this substitution is limited. Other substitution possibilities are also very limited. These results suggest that the current configuration of electricity generating assets is inflexible and that to achieve substantial reductions in carbon emissions more flexibility must be introduced, most likely from significant investments in new generation capacity.

18
set is particularly well suited for understanding the dynamics of farm productivity, with detailed information on production inputs, outputs, and costs, allowing for better measurement of the key variables needed for the analysis. For example, for every crop grown by a household, the survey data provide price, quantity, and other details on land preparation, application of seed, fertilizer use, irrigation, weeding, pesticides, crop damage, and labor used in harvesting and threshing. The data set also describes how different types of labor were used for cropping activities and marketing of outputs. The data set also provides extensive details about farms’ characteristics, such as soil type, elevation, irrigation sources, and tenurial arrangements.

# **4. Results and Discussion**

The descriptive statistics for the panel of farm households used in this analysis are given in Annex Table A.1. In the three rounds of data analyzed, 2004 stands out as a relatively bad production year because of flooding in large parts of the country. The impact of floods, which typically affect the rainy season crop, appears to have been significant – evident not only in lower output per hectare but also lower intensity in the use of inputs in 2004 relative to the other years. The years 2000 and 2008 were normal, with data showing strong improvement in performance over the eight years. Output rose significantly faster than inputs between 2000 and 2008, suggesting strong growth in productivity.

Other important points to note include the widespread use of irrigation, which is expected to positively impact yields and provide stability with better water management. The practice of leasing in land (either on sharecropping or rental arrangements) is also widespread: the share of households leasing in at least some land to supplement their own land is over 50%. Sharecropping is more popular than other rental arrangements. On average, about one-third of the land cultivated by households is leased in each year, and the practice and shares are consistent for all land classes, including for the landless and functionally landless households. Finally, an important characteristic is the high level of land fragmentation, with a large number of small, distinct, and often widely dispersed plots operated by households. These features of the data are important as they help in interpreting some of the estimation results reported below.

Even though the absolute farm sizes in Bangladesh are small, there is still substantial disparity in farm sizes and a skewed farm size distribution. Table 2 shows that average and median farm sizes 14