rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Composition of Output**

|**Dependent variable:**||||
|---|---|---|---|
|**Ln (Output)**|**(1)**<br>**OLS**|**(2)**<br>**FE**<br>**(industry-**<br>**level)**|**(3)**<br>**FE**<br>**(dirty/clean)**|
|Air Pollution|0.255***|0|0.381***|
||[2.85]|[1.31]|[5.10]|
|Water Pollution|0.102**|0|0.134***|
||[2.06]|[0.54]|[3.35]|
|Toxic pollution|0.351***|0.000**|0.531***|
||[3.69]|[2.45]|[6.48]|
|Labor Productivity|-0.000**|0|0|
||[2.40]|[1.35]|[1.61]|
|Capital Productivity|0.111|0.160*|-0.002|
||[1.57]|[2.00]|[0.03]|
|Constant|-7.915***|-0.939***|-11.867***|
||[7.63]|[5.70]|[11.12]|
|Observations|66|66|66|
|R-squared|0.53|0.71|0.6|
|Number of VSIC||22||
|Number of Dirty_dum|||2|

Absolute value of t statistics in brackets

* significant at 10%; ** significant at 5%; *** significant at 1%

Descriptive analysis in Figure 3 showed that water pollution intensive output had shown the greatest percentage increase. Regression results using OLS (column 1) and fixed effects (column 3) also show that water pollution intensive output showed a positive and significant increase during this period, although the size of the coefficient on water pollution intensity is smaller than air and toxic pollution intensity. We do not find consistent results on labor and capital productivity variables.

Next, we examined the changes in composition of overall exports and changes in composition of exports in the post-USBTA period. The regression results on overall change in export composition are shown in Table 3. As in Table 2, column (1) shows 19
15

# _Spot market trading_

Spot markets have developed at the onshore terminals of BGT's pipeline network as a result of the increasing need to balance supply and demand in the short term.' Four main factors are behind the development of spot market trading in the U.K.:<sup>6</sup>

1. Introduction of the 90:10 rule in 1989. This rule prohibits British Gas from purchasing more than 90 percent of the gas from any field. After it was introduced, producers started to market their gas to independent gas suppliers, which competed with British Gas in the contract market. Producers initially sold gas under medium- or long-term contracts. But as changing demand and supply conditions led to a need to balance gas requirements in the short term, they started selling gas on a short-term basis.

2. Ofgas order to British Gas to release gas to competitors in 1991. Between 1992 and 1995 British Gas made about 4.9 billion cubic meters of gas (this is total volume over the whole period) available to competing suppliers through sale in spot markets.

3. Oversupply by independent suppliers. Some suppliers committed themselves to 100 percent take-or-pay contracts but could not find customers for their gas. They took the surplus gas to spot markets for resale.

4. Delay in the start-up of three power stations. This delay led to an estimated demand loss of 6.2 million cubic meters a day, or 3 percent of the average daily demand. Stuck with the surplus gas, producers took it to spot markets.

Trading in a spot market is done on a bilateral basis, between producers and shippers, or on a brokerage basis, with traders often acting as intermediaries. In the U.K. spot market trading started as a bilateral telephone market in 1989-90 between producers and suppliers (Roeber 1996). The nature of the trading has not changed substantially except for the entry of traders and the higher volumes of trading. Producers, shippers, and traders "shop around," arranging for terms that best suit their needs.

Five types of natural gas contracts are traded in the U.K. spot markets:

-

- * Day-ahead gas for delivery on the next day.

-

- * Balance gas for delivery on a daily or weekly basis.

-

- * Monthly gas for delivery during a specified calendar month.

> ' There are six entry terminals in the U.K., but only two of them, Bacton and St. Fergus, have developed into major spot markets.

> **6** The data in this section are drawn from Royle 1996.
6 than in production, this leads to the underestimation of early-career wages, implying an overestimate of the returns to experience due to learning-by-doing only, particularly in developed economies where more time is spent in training. Given that we do not have information on how much time each worker in our data spends working or training, we cannot directly assess the contribution of each mechanism. However, indirect robustness checks based on cross-country data on the number of hours spent in formal or informal training suggest that our results are more consistent with learning-by-doing than the learning-versus-doing model of Ben-Porath (1967).

# **2. Data Used to Estimate the Returns**

**Sources.** The data source for the analysis is the _International Income Distribution Database_ (I2D2) of the World Bank. The database consists of a large number of individual-level surveys and census samples. The surveys include household surveys and labor force surveys. The data was initially compiled by the World Bank’s _World Development Report_ unit between 2005 and 2011. The original version was used by Montenegro and Hirn (2009) to study the labor market characteristics of developing countries for the 2009 _World Development Report_ of the World Bank. The database has since been expanded and used by some of the members of the original _World Development Report_ unit.<sup>7</sup> We use the expanded December 2017 vintage of the I2D2 database. Only select members of the research team or individuals in charge of harmonizing the data can access the database. However, a significant number of the surveys can be accessed online or after entering an agreement with the countries’ respective statistical office. Finally, the surveys are nationally representative and large enough (i.e., typically have more than 10,000 observations) for our purpose.

**Sample Size.** The version of the I2D2 database that we use includes about 1,500 survey/census samples. However, wages are only reported for about two thirds of them. In addition, we restrict our analysis to workers aged between 18 and 67 with data on age and education as well as samples with at least 10 observations in each experience bin (see details below) to ensure results are not driven by small samples.

The baseline sample that we obtain includes 24,437,020 individuals from 1,073 surveys and 11 censuses in 145 countries from 1990-2016 (median number of samples 7The I2D2 database has been used to study labor markets or returns to education (Montenegro and Patrinos, 2014; de Hoyos et al., 2015; Gindling and Newhouse, 2014; Gindling et al., 2016).
Figure 4. Variation in employment according to branch of activity. (In percentage points)

# a) Argentina - 1998 – 2015

<!-- Start of picture text --> 4.0%<br>3.0%<br>2.0%<br>1.0%<br>0.0%<br>-1.0%<br>-2.0%<br>-3.0%<br>-4.0%<br><!-- End of picture text -->

# b) Uruguay - 1995 – 2015

<!-- Start of picture text --> 8.0%<br>6.0%<br>4.0%<br>2.0%<br>0.0%<br>-2.0%<br>-4.0%<br>-6.0%<br>-8.0%<br>-10.0%<br>-12.0%<br>-14.0%<br><!-- End of picture text --> Source: Our own estimation based on household surveys.

All these transmission channels, in particular that associated with the movement of workers between occupations, are enhanced by an increase in the educational level of the labor force, increasing the possibilities of changing occupation for those who are already active in the

- 19 -
# **I. Introduction**

A large fraction of the total supply of paper is produced using technologies that have serious adverse consequences on the environment and human health. For example, in the United States the pulp and paper industry ranks third in air, water, and land pollution, following the chemical and primary metals industries (National Pollutant Release Inventory, 1996). The pulp and paper industry also represents the fourthlargest global industrial energy user (6.4 EJ in 2005) as well as a significant source of greenhouse gas emissions.<sup>2</sup> Paper production generates greenhouse gas emissions both directly via the production process and indirectly via greenhouse gas emissions associated with the off-site generation of steam and electricity used by paper mills (Energy Information Administration, 2009). Furthermore, the dioxins released in the production of paper increase the likelihood of contracting various types of cancer (Thompson et al. 2001; Sumathi and Hung 2006, World Health Organization 2010).<sup>3</sup> State agencies and non-governmental organizations have invested substantial effort and resources in order to ameliorate the negative effects that paper production imposes on both the environment and human health. These efforts have mainly focused on promoting the development and use of cleaner production technologies as well as changing and enforcing regulations (e.g., the Clean Air Act in the United States). However, paper production and therefore pollution may also fall when the demand for paper decreases, and then it becomes important to examine the factors affecting paper consumption.

In this paper we focus on examining how Internet adoption affects paper consumption. There are various ways by which the Internet may displace paper consumption. For example, reading online newspapers and magazines, adopting

> 2 http://www.iea.org/publications/freepublications/publication/Indicators_2008.pdf 3 http://www.who.int/mediacentre/factsheets/fs225/en/

2
Building on previous studies, our paper offers three main contributions. Firstly, we explore the effect of severe drought conditions on the labor market. Using the Standardized Evapotranspiration Precipitation Index (SPEI), we identify the Moroccan provinces that have been hit by extreme drought events and frame the analysis in a staggered difference-indifferences setting in order to estimate the effects on three outcomes: the employment rate in the agri- culture sector (including the most climate-sensitive activities); the employment rate in other sectors; and the unemployment rate. This provides an overall picture of the labor market dynamics at the local level for a period of up to five years after the drought’s shock.

Secondly, we have conducted a rich analysis of the effects of heterogeneity by disentangling the impacts across age, gender, educational levels and formal/informal work participation for each of the three outcomes considered. Therefore, our analysis also speaks to the important strand of literature on climate injustice, which highlights how climatic shocks tend to have a significant distributional impact with unequal effects among the most vulnerable and most exposed population groups (Sovacool, 2013). Finally, we explore how drought events of different intensities affect the labor market; this provides a useful picture for understanding how the market responds to drought shocks of increasing severity, and how the policy response should be targeted accordingly.

Our estimates show that in Morocco severe drought events, measured by a 12-month SPEI lower than -2 s.d., caused a drop of up to 6.5 percentage points (p.p.) in the agricultural employment rate compared to provinces that were not affected by severe drought. In the same period, we also observe an non-significant increase of 4.8 p.p in the employment rate of other economic sectors, and a significant increase in the unemployment rate of nearly 2.5 p.p. Even though our data do not allow observing the same workers over time, overall, those figures provide strong evidence of a large displacement of agricultural workers and a 6
developing countries and their small profit-margin industries – i.e., firms for which it is unlikely that sufficient extra resources are available to coordinate politically and engage in the self-monitoring that may occur through trade associations or national governments on their behalf. Within the supply side of new trade-restricting policies to consider, our decision on where to apply additional analytical public resources is guided by a focus on potential WTO violations of _national treatment_ , or a policy that discriminates between domestic firms and foreign firms. These are the policies likely to affect many diffuse exporters; i.e., policies to which challenges are otherwise least likely because the coordination across many foreign interests is most difficult.

Before turning to the three categories of policies identified above, it is worth repeating that the data and information generated by initiatives such as the GTA, Global Antidumping Database, and the WTO Secretariat are still primitive and merely an input for _others_ to use. These sources admittedly provide little information on the key questions (1) through (4) identified above. Teams of economists, lawyers and political analysts with technical expertise must work together to match up the primitive data from these sources with more detailed information on trade flows, market conditions, and industry and firm performance. With respect to any potential trade barrier identified by these initiatives, exporting firms and their government policymakers require answers to these four questions in order to make the larger expected cost–benefit calculation. Without this information they will be unable make a rational determination of whether to raise the issue of a potential WTO violation with trading partners; and subsequently whether to bring this matter to the WTO, potentially for dispute settlement.

21
**Table 5: School Construction and Child School Readiness (EDI): Difference-in-differences estimates.**

||1966-1970|Maternal Cohort|Differe|nce in Dif|ferences|
|---|---|---|---|---|---|
||High INPRES|High-Low INPRES||||
||<br>Mean|<br>Gap|[1]|[2]|[3]|
|Physical health and well-being|0.05|0.12|-0.047|-0.039|-0.038|
||||(0.087)|(0.086)|(0.088)|
||||[0.616]|[0.644]|[0.700]|
|spaceChild in bottom quartile of age-specifc physical health and well-being (%)|28.7|-4.0|-3.651|-4.393|-4.319|
||||(4.618)|(4.446)|(4.655)|
||||[0.440]|[0.372]|[0.368]|
|Social competence|-0.07|-0.19|0.198|0.219|0.217|
||||(0.069)|(0.064)|(0.065)|
||||[0.012]|[0.000]|[0.000]|
|spaceChild in bottom quartile of age-specifc social competence (%)|30.3|11.1|-7.549|-8.075|-8.081|
||||(2.474)|(2.229)|(2.223)|
||||[0.052]|[0.004]|[0.024]|
|Emotional maturity|0.09|0.12|0.061|0.086|0.085|
||||(0.062)|(0.053)|(0.052)|
||||[0.332]|[0.100]|[0.160]|
|spaceChild in bottom quartile of age-specifc emotional maturity (%)|25.5|-4.6|-1.077|-2.075|-2.022|
||||(2.666)|(2.685)|(2.720)|
||||[0.688]|[0.416]|[0.440]|
|Language and cognitive development|-0.08|-0.14|-0.029|-0.016|-0.017|
||||(0.084)|(0.083)|(0.084)|
||||[0.700]|[0.880]|[0.876]|
|spaceChild in bottom quartile of age-specifc language/cognitive development (%)|30.5|8.3|-4.322|-4.774|-4.655|
||||(3.301)|(3.341)|(3.293)|
||||[0.264]|[0.132]|[0.220]|
|Communications and general knowledge|-0.03|-0.14|0.033|0.045|0.043|
||||(0.072)|(0.072)|(0.074)|
||||[0.628]|[0.528]|[0.592]|
|spaceChild in bottom quartile of age-specifc commun./knowledge (%)|33.0|1.6|3.054|2.413|2.55|
||||(3.749)|(3.824)|(3.692)|
||||[0.456]|[0.600]|[0.584]|
|_Controls (D-in-D’s)_||||||
|<br>Caregiver district and year of birth FE’s|||YES|YES|YES|
|Treated cohort interaction with # school-aged children in 1973 (ln scale)|||NO|YES|YES|
|Child gender|||YES|YES|YES|
|Child-age FE’s|||YES|YES|NO|
|Child’s age(linearly,in months)|||NO|NO|YES|

Notes: Sample is 2,118 children. Standard-errors in parentheses are clustered at the district of maternal birth. Wild-bootstrap significance p-values in brackets.
the use of the assigned share of revenue. Additionally, PB may mean slightly different things in different municipalities. Finally, the large municipalities for which I have PB data could be unique, casting doubt on the validity of the results in less-urban contexts. I address these concerns in this section by considering a second measure of equal access to political power for which I have data for all municipalities.

If the poor still have less political power than the rich under PB, my analysis would underestimate the importance of the political power channel. Ideally, I want to examine the effects of income and inequality on revenue use in political spheres where the poor have as much influence as the rich. As a robustness check, I divide the sample into municipality-years with socialist party mayors and those without, and estimate the same model. On average, about 17% of municipalities have a socialist party mayor, defined as an elected mayor whose party affiliation is one of the following five: PDT, PT, PSB, PPS, or PCdoB.<sup>40</sup> These are left-wing, grass-roots parties. There is not a significant relationship between per capita expenditure and having a socialist party mayor, after controlling for municipality fixed effects, median income, and population. However, these parties have historically pursued policies aligned with what blue-collar workers indicate they want in opinion polls. Socialist party mayors are likely to be elected precisely because the poor are equally- or relatively more-politically involved compared to the rich. An advantage of this analysis is that I have accurate, time-varying data for the full sample. I leverage off of within-municipality changes in the party in power over time.

Appendix Table A.2 presents these regression results. I examine the same three dependent variables as in the PB analysis, and the findings are nearly identical. Whether or not there is a socialist party mayor, richer municipalities are less likely to use an exogenous revenue increase to boost education spending or pre-primary enrollment, and more likely to improve pre-primary quality. These results are always significant at the 0.01 level. Again, the estimates on income interacted with revenue are significantly smaller in magnitude (closer to 0) for the sub-sample of municipalities with socialist party mayors (for which the political channel is suppressed) than

> 40In order of frequency, the Democratic Labor Party (Partido Democr ́atico Trabalhista, PDT), Workers’ Party (Partido dos Trabalhadores, PT), Brazilian Socialist Party (Partido Socialista Brasileiro, PSB), Socialist Popular Party (Partido Popular Socialista, PPS), and Communist Party of Brazil (Partido Comunista do Brasil, PCdoB).

30
6

**Table 1 Financial Development Ratios**

|Ratio|1951-52 to|1966-67 to|1980-81|1989-90|
|---|---|---|---|---|
||1955-56|1968-69|||
|Finance ratio**(%)**|4.9|13.8|32.7|43.9|
|Financial interrelation ratio|0.63|0.93|1.93|2.50|
|Intermediation ratio|0.27|0.33|0.41|0.45|

Source: Rangarajan and Jadhav (1992) Notes: (I) Finance ratio = total financial claims/national income; (ii) Financial interrelation ratio = increase in stock of financial claims/net capital formation; (iii) Intermediation ratio = claims issued by financial institutions/ issues of non-financial sectors.

However, the fact that financial institutions are largely publicly owned and guided by so called "public interest considerations" is likely to have led to serious incentive problems and to a misallocation of resources. These institutions are not under pressure to rush to foreclose a mortgage when a borrower defaults. Thus loans are likely to be re-scheduled more often than if the financing institution were guided purely by commercial motives. For this reason, liquidation even of insolvent firms was (and continues to be) quite rare in India. This tendency is reinforced by legislation that makes liquidation and exit quite difficult. Moreover, there are also a large number of cases of perverse loaning where loans are extended to firms that are insolvent and not viable. For instance, in 1991 an estimated 7% of bank lending was to medium and large "sick" units (see World Bank (1991)). Such lending merely finances the servicing of prior loans and extends the life of non-viable firms. The extent to which short term loans are often rolled over makes them indistinguishable in many cases from long term loans. Despite efforts over the last few years to reduce them, even today nonperforming assets (NPAs) form about 20% of the asset portfolio of commercial banks. The NPA burden of DFIs was and continues to be lower. The large burden of non-performing loans limits the
force are both outcomes of some unobservable determinants. Examples of such unobserved determinants could be the education or labor force participation of mothers, influences of peers, or a more general preference for work that motivates both higher education and labor force participation. This selection effect might change over time. If the average woman completing secondary or higher education in 2011 is less positively selected than her 1987 counterpart, this could potentially account for the declining effects of secondary and graduate education between 1987 and 2011.<sup>23</sup> A detailed analysis of the determinants of women’s educational attainment would require data on women’s parental background and on schooling supply and other characteristics of the location where they grew up. Unfortunately, those data are not available, as we only observe married women in their husband’s household—which is often in a different area than where they grew up—and have no information on their location of origin or on their parents’ education and employment. The following section provides supporting evidence, however, for the empirical relevance of the selection channel, which is also consistent with an analysis by cohorts we discuss next.

Among the factors that could explain a decrease in positive selection are the increasing supply of education and rising marriage market returns to education: both would be reasons for women to pursue more education even in absence of expected labor market returns and unrelated to the unobservable propensity to join the labor force. Given the high pace of expansion of schooling supply, it is likely that highly educated women in 2011 are less positively selected than highly educated women in 1987.<sup>24</sup>

> 23 Another explanation would be a decline in the quality of education. Azam and Kingdon (2013) study gender bias in education expenditure and show that in the period 1993–2005, when girls’ education caught up to boys’, households still spent less on girls, primarily by sending girls to public schools and boys to private schools. Girls are thus likely to receive lower quality schooling than boys, even if they attain the same level of education. Unfortunately we do not observe the type of institution a woman attended or any other indicator of the quality of education in the NSS data. 24 As discussed in Section 2 some recent studies show that (girls’) enrollment responds to the growth of jobs in IT and IT enabled services (Jensen 2012; Shastry 2012; Oster and Millett Steinberg 2013), but this is a very recent development and this sector still accounts for a very small share of employment. We are not aware of any research on

30