rafmacalaba/datause-encoder-data huggingface dataset:
_Figure 3. Enrollment and school exit rates, by age and country_ _Source:_ Authors’ tabulations using LABLAC (CEDLAS and the World Bank). _Notes_ : Figure 3(a) presents the share of the urban population at each age that is enrolled in school. Figure 3(b) presents share of individuals that leave school in any given year by age (enrolled in year _t_ and not in year _t+1_ ). Estimations were calculated pooling all the data over the period 2005-2015.

Figure 3(b) shows that school exit rates for Mexican students are lower than those of the other two countries around the ages when secondary school is completed. This reflects, in large part, the observation made earlier in Figures 2 and 3(a): approximately 20 percent of Mexican urban youth have already exited school by the age of 15, a much higher number than in Argentina and Brazil (under 10 percent). Since enrollment and completion rates of secondary education in Mexico are significantly lower than in Argentina and Brazil, it suggests that more of the “cream skimming” may be happening between basic education and upper secondary school. If each individual _i_ has some probability of exiting as a response to an income shock, the lower enrollment rate may suggest that more students with higher probability of exiting school in response to a shock have already exited by the age of 15 in Mexico as compared to Argentina and Brazil. This would leave individuals with lower probability of exiting school overrepresented in the Mexican 15-25 population relative to the other two countries.

Figure 4 confirms that the attrition from the population of enrolled youth is different in Mexico as compared to the other two countries; it results in significantly lower enrolment rates among students in lower income groups in Mexico relative to Argentina and Brazil. Figure 4 shows the distribution of the sample of enrolled youth across income deciles in the three countries. While in Argentina and Brazil, students are disproportionately more likely be in the second through fourth deciles, in Mexico students are disproportionately likely to come from the top half of the income distribution. That is, there are larger gaps in enrollment rates between low and high-income youth in Mexico compared to the other two countries.

12
Table 2: Employment Effects of the Tax Cut

||(1)<br>All frms|(2)<br>Low TFP|(3)<br>High TFP|
|---|---|---|---|
|**Panel A: Change in private sector employment **|**probability**|||
|— After _×_ Treated|0.0053<sup>_∗∗∗_</sup>|0.0053<sup>_∗∗∗_</sup>|-0.0001|
||[0.0005]|[0.0005]|[0.0004]|
|**Panel B: Percent change in employment**||||
|—Employment without tax cut|0.330|0.167|0.163|
|—Employment with tax cut|0.335|0.172|0.163|
|—Percent change in employment|1.59%|3.18%|-0.03%|
|**Panel C: Percent change in labor cost** (1 +_τss_)||||
|—Labor cost without tax cut|1.27|1.26|1.28|
|—Labor cost with tax cut|1.20|1.18|1.22|
|—Percent change in labor cost|-5.27%|-6.02%|-4.45%|
|**Panel D: Implied elasticity (Panel B/Panel C)**||||
|— Elasticity|-0.30|-0.53|0.01|
||[0.03]|[0.05]|[0.06]|
|_∗p <_0_._1, _∗∗p <_0_._05, _∗∗∗p <_0_._01||||

_Note_ : Panel A of the table shows difference-in-differences estimates of the impact of the payroll tax cut on private sector employment for all firms (column 1) and separately for below-median (column 2) and above-median (column 3) TFP firms in Panel A. In particular, we report the _β_ coefficient from regression equation (9) with the outcome variable being employed at a private sector firm (column 1), at a private sector firm with below-median productivity (column 2) and at a private sector firm with above-median productivity (column 3). The _β_ coefficient estimates the change in employment among the 55 to 57 age group that was affected by the payroll tax cut relative to the change in employment among the 52 to 54 age group that was not affected by the tax cut. Panel B calculates the percent change in employment using the difference-in-differences estimates from Panel A. The first row shows the employment rate in the treatment and control age groups in 2012, the year before the introduction of the payroll tax cut. The second row adds to that baseline the estimated change from Panel A. The third row shows the percent change in employment relative to the baseline. Panel C calculates the percent change in labor cost using an analogous difference-in-differences estimation for tax rates. Firms’ labor cost is net wage times (1 + _τss_ ), where _τss_ is the employer social security contribution. The reform cut _τss_ for workers in the treatment group. The first row shows the average labor cost in the control group between 2013-2015. The second row calculates the average labor cost for the treatment group taking into account the tax cut. The third row calculates the percent change in the labor cost caused by the tax cut. Panel D calculates the implied employment elasticity with respect to the wage change by taking the ratio of the percent change in employment (Panel B) and labor cost (Panel C). Standard errors are reported in brackets, clustered at the age _×_ period level. ( _N_ = 9 _,_ 003 _,_ 984 individual-months) 55
Recently, Bolch et al. (2008b) and ICIMOD (2011) identified potentially dangerous glacial lakes in Nepal. Bolch et al. (2008 b) categorized hazard as low, medium, and high. Lakes in Imja Glacier and Chukung Glacier are rated as medium hazard; lakes southwest of Baruntse and Hunku Glacier are rated as low to medium hazard lakes (Bolch et al., 2008 b). Watanabe et al. (2009) detailed Imja Tsho’s evolution from 1956-2007 and gave details on the Imja GLOF risk. Similar monitoring and risk assessment studies have been carried out in the Bhutanese Himalayas (Ageta and Iwata, 1999; Ageta et al., 2000; Fujita et al., 2008, Komori, 2008; Komori et al., 2004; Leber et al., 1999). Based on field investigations and inventory in the middle section of the Chinese Himalaya, Xu and Feng (1994) reported 139 moraine-dammed glacier lakes, of which 34 were identified as dangerous. They identified ice avalanches from advanced glacier tongues and ablation of dead ice beneath moraine ridges as potential GLOF triggers.

# **3. Future Climate Projections**

# **3.1 Temperature**

Global Climate Model (GCM) projections point to a warmer Himalayan region in the future with warming likely to be above the global average. The Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4) presents temperature and precipitation projections for South Asia, derived from a dataset of 21 GCMs, suggest a median increase of 3.3 °C by 2100 for the A1B SERS scenario (a ‘‘middle of the road’’ estimate of future conditions; Nakićenović et al., 2000), with increases in both daily minimum and maximum temperatures (Christensen et al., 2007). As Christensen et al. (2007) point the largest warming is expected on the Tibetan Plateau and the higher-altitude Himalayan regions: 3.8 °C during the next 100 years. For South Asia, the median warming varies seasonally from 2.7 oC in June-August (monsoon) to 3.6 oC in December-February (winter). For the same scenario, the seasonal variation in the simulated warming in Xizang province of China ranges from 3.6 oC in March-May to 4.1 oC in December-February.

Despite general consensus on importance of GCM projections, due to their large spatial scale (e.g. hundreds of kilometers) they are not able to capture local or regional information, such as orographic features and summer monsoon considered extremely important in the 10
This apparent convergence, however, might be transitory. Figure 7 shows the same scatterplot during the slowdown period but with a different classification of countries: “the Reformers”. These are countries for which the Global Competitiveness Index (published by the World Economic Forum) has increased at a faster pace than the worldwide average increase from 2010 to 2015. Under this definition, the reformers have become more competitive by improving a set of institutions, policies, and factors (e.g., infrastructure, health, and education; macroeconomic stability; and well-functioning labor, financial, and human capital) that determine the level of productivity in their economies (WEF 2016). These reformers appear to have achieved larger poverty gains that non-reformers during 2012-14. This classification suggests a new split in the region that is worthy of analysis, and leaves behind the old “commodity”–“non-commodity” classification.

_Figure 7: Is there a new split in the region in terms of poverty performance?_ Slowdown (2012-14 annual average) <!-- Start of picture text --> 2<br>0<br>-2<br>-4<br>"Reformers"<br>-6<br>-1 1 3 5 7 9<br>GDP Growth (%)<br>Poverty change (ppts)<br><!-- End of picture text --> Source: LAC Equity Lab tabulation using SEDLAC data (World Bank and CEDLAS). “Reformers” are defined as countries which saw their Global Competitiveness Index (World Economic Forum 2015) increase at a faster pace than the worldwide average from 2010 to 2015. The countries identified as reformers are Colombia, Dominican Republic, Ecuador, Mexico, Nicaragua, Peru, and Paraguay.

# **3. What is behind recent trends?**

While the previous section focused on exploring what has been happening with poverty and inequality in LAC since the beginning of the slowdown, this section explores some of the drivers behind these trends. How does slowing economic growth impact household welfare? The labor market and fiscal policy are the two key transmission channels between economic growth and household income (Figure 8). The labor market, the most important channel of transmission due to the important role of labor income in household budgets, reflects the interaction between firms and employees through wages (prices) and employment (quantities). These two components are 9
Kaganova 2010). Although part of this pattern can be explained by a lack of focus on market valuations by public owners, the relationship also opens the way for public officials and public agencies to exchange below-market rents for greater political power, corrupt payments, or other mutually beneficial special arrangements.

By the criteria of Value for Money applied in Australia, the failure to obtain market rents in leasing deals becomes a reason for divestiture of public lands under the Land Audit. In fact, there is a general presumption against holding public land for lease to private industrial firms. The practice sets up the potential for rent-seeking officials to enrich themselves and/or pursue political objectives by manipulating lease awards, while keeping land-use decisions out of the marketplace.<sup>2</sup> Of course, similar incentives exist when land is sold by a public agency, but they are more easily controlled by a one-time, transparent public auction.

# **Charges and Taxes As Incentives to Reevaluate Public Landholdings**

In principle, the opportunity costs to public agencies of holding valuable land could be brought home to them by a system of charges or taxes levied on the value of land. Both Australia and South Africa have launched initiatives that could expand into this area, though neither has significant impact at present on the decision whether or not to make land available for disposition.

**Rental Charges for Public Property.** Australia has a well-established policy of charging public agencies the full market rental rate for their occupancy of government buildings.   The policy is overseen by the Property Division of the Ministry of Finance and Deregulation, and administered on the Ministry’s behalf by a private-sector property management firm.  Public agencies are not required to use public buildings; they are free to search the market for the most cost-effective locations.  However, if they occupy public buildings they must pay full market rentals, a policy also pursued in several other countries.  The rental value of office buildings, of course, reflects their location and underlying land values.  Thus the system provides some incentive to public agencies to economize on public land use—i.e., only to occupy public land that is worth its rental price.

Australia has not extended this rental pricing system to vacant landholdings, preferring instead to rely on the Land Audit to recognize market opportunity costs and drive disposal decisions.

> 2 In a very interesting study of the differences in the rate of privatization of industrial land across Russian cities and regions, Pyle (2011) finds that, paradoxically, the rates of public land ownership and leasing are highest in those places with the highest urban land values.  He argues that public retention of land ownership in these cases maximizes political influence and rent-seeking potential, and that this is the motivation for leasing rather than sale.  In fact, when industrial enterprises were privatized, the Moscow Duma passed legislation stipulating that the underlying land plots could only be leased, not sold.

16
# Trade, Emissions, and Environmental Spillovers: Issue linkages in Regional Trade Agreements<sup>∗</sup>

Clark Lundberg<sup>†</sup> , Daniel Szmurlo<sup>‡</sup> , and Ryan Abman<sup>§</sup>

**Keywords:** Climate Change, Ozone Depleting Substances, Regional Trade Agreements, Trade Liberalization, Montreal Protocol, Environmental Policy

**JEL Classification Codes:** F14, F18, Q53, Q56

> ∗We are grateful to discussion from Mauricio Mesquita Moreira and Bill Ridley, as well as participants in the 2022 World Bank Deep Trade Agreements Conference, the 2022 AAEA Annual Meetings, and the 2022 WEAI Annual Meetings. This paper has benefited from support from the World Bank’s Umbrella Facility for Trade trust fund financed by the governments of the Netherlands, Norway, Sweden, Switzerland and the United Kingdom. The findings and ~~conclusions in this article are those of the auth~~ ors and should not be construed to represent any official USDA or US Government determination or policy. All errors are our own.

> †Department of Economics, San Diego State University: clundberg@sdsu.edu. Co-lead author order randomized.

> ‡Economic Research Service, US Department of Agriculture: daniel.szmurlo@usda.gov. Co-lead author order randomized.

§Department of Economics, San Diego State University: rabman@sdsu.edu.
with cooking. The maximum ambient temperature in the sample, measured using thermometers, is 35 degrees Celsius and the average measured ambient temperature is 25 degrees.

Using signal processing and analysis (O'Haver, 1997) we identify all cooking events and then calculate their durations. Cooking event duration is calculated as the total time in minutes starting from when the temperature is 40 degrees Celsius at the beginning of an identified cooking event until it comes down again to 40 degrees Celsius at the end of the event. As noted above, because the SUM record temperatures only every 10 minutes, there is likely some measurement error. This error is bounded and approximately 10 minutes in total out of about an hour average CCT cooking event time, assuming 5 minutes error on each of the starting and ending time measurements.

More importantly, because on average households use their Mirt stoves 15 times per measurement round and most households are measured over four rounds, these measurement errors average out. Moreover, we clearly know the direction of the bias. Measurement error can only <u>reduce cooking duration, implying that our per-minute savings from the three rounds of CCTs are</u> applied to <u>fewer measured cooking minutes. The measurement error therefore has the effect of</u> making our estimates conservative.

The SUM devices were initiated and set to record the temperature of the stove every ten minutes. This was done four times over the course of more than one year beginning in June 2013 and ending in July 2014. The four monitoring periods are June-August, 2013; August-October, 2013; March-May 2013 and May-July 2014. During each monitoring period the SUM devices recorded data for between 37 and 56 days. The median recording period was 49 days.

CCT data that match the timing of the monitoring periods were used for the first three monitoring periods. As we have four SUMS monitoring periods and only 3 CCT rounds, in the fourth SUM monitoring period the third round CCT per-minute fuelwood savings were applied to the fourth round SUM data. This approach is likely conservative, because on average total fuelwood savings from the CCTs monotonically increase and cooking time monotonically decreases over time. These together suggest improved performance over time, albeit at a decreasing rate between rounds 2 and 3.

We collect CCT data only for 3 of the 10 households in each of the 36 villages that actually received Mirt stoves and were monitored using SUM. This is because conducting the CCTs is

**15 |** P a g e
**Figure 3: Discovery Concentration and Performance**

<!-- Start of picture text --> POL<br>JORIDNISRMUSCOLMARHUNINDOMNBOLGTM<br>IRL URY SLVNICPERTURHKGCRIDZAARGDNKCHLECUPRYEGYBRAGRCPRTMYSHNDCYPTGOKOR VENAUSPAN GBR<br>NZL MDGCAN DEU<br>MWIMEX<br>JPN<br>NOR<br>FIN<br>AUT ESP ITA<br>SWE FRA<br>SGP NLD CHE<br>USA<br>.2 .4 .6 .8 1<br>concentration<br>Leamer commodity groups, only observations included in estimation are shown.<br>Figure 4: Export Boom Concentration and Performance<br>TUR<br>POL<br>ESP IND HUN<br>KOR<br>PRTMYSMEXHKG DNKCANSWEBRA AUT<br>GTMNLDGRCNORNZLCHEIRLARGITAIDNFRAAUSFINCOLISRPERSGPMARJPNCHLUSACRI<br>DEU<br>GBR SLVMUS<br>ECU<br>VEN<br>NIC<br>BOL<br>CYP<br>HND<br>TGO OMNMDG JORPAN<br>URYPRY<br>DZA<br>.2 .4 .6 .8 1<br>concentration<br>6<br>4<br>performance<br>2<br>0<br>5<br>4<br>3<br>performance2<br>1<br>0<br><!-- End of picture text --> Source: Authors’ calculations. Performance is log of discovery counts (given the implicit log-linear specification of the negative binomial regressions presented below), concentration is the Gini across Leamer commodity groups, only observations included in estimation are shown.

Source: Authors’ calculations. Performance is log of boom counts (given the implicit log-linear specification of the negative binomial regressions presented below), concentration is the Gini across Leamer commodity groups, only observations included in estimation are shown.

21
9

**Figure 4. Evolution of unemployment rates in the global crisis-affected countries, %**

<!-- Start of picture text --> 20<br>Total unemployment rate<br>UK<br>18<br>US<br>16<br>GIIPS<br>14 Other Euro<br>12<br>10<br>8<br>6<br>4<br>2<br>45<br>Youth unemployment rate UK<br>40 US<br>GIIPS<br>35<br>Other Euro<br>30<br>25<br>20<br>15<br>10<br>5<br>9<br>Long term unemployment rate UK<br>8<br>US<br>7 GIIPS<br>Other Euro<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> _Source_ : Authors’ computations, based on World Bank World Development Indicators; Eurostat Unemployment Statistics. GIIPS stand for Greece, Ireland, Italy, Portugal and Spain.
Surprisingly, there is agreement about when assimilation and contrast effects emerge. As Schul and Schiff (1993) note, "[r]esearch has shown that the impact of early specific questions have on the responses to a later general question is influenced by the number of specific questions and their positioning in the survey. [Wihen a general question is preceded by a single specific question, responses to the general question show a contrast effect unless the specific and general questions are separated." Sudman, Bradbum, and Schwarz (1996) note that asking the specific questions first yields assimilation effects when there are unrelated questions between the specific and general questions. Even graphically separating the specific and general questions on a self-administered survey or putting the specific and general questions in different sections of an interviewer-led survey generated assimilation effects. The contrast and assim.ilation effects are more intense the more sweeping or ambiguous is the general question.

To sunmnarize, a specific question followed immediately by a general question tends to generate a contrast effect, while a specific question followed by several unrelated questions or a section break and _then_ a general question tends to generate an assimilation effect. A general question asked before specific questions or completely absent specific questions may reflect contrast or assimilation effects, but the researcher has no way to know which. The survey can minimize such problems by avoiding ambiguity as much as possible and by asking the respondent to answer general questions either to reflect his experiences or actions or what he believes the general condition to be relative to his experiences or actions. That is, ask for a contrast or assimilation of specific questions posed first.

# **Recent and less-recent behavior**

Often the researcher is interested in learning about recent and typical behavior, which are not always identical. Surveys frequently ask whether a respondent has done something within the past x weeks (or months or years) and then within the past x + y weeks. It turns out that asking about the shorter time period first tends to lead respondents to over-report that activity (Fowler 1995). This over-reporting may happen because of the respondent's desire to report

> contrast or assimilation effects. Assume 75 percent of the second sample believed its representative was honest. If 10 percent of this second sample believed that politicians in general were honest, it could be an example of a contrast effect, while if 30 percent of this second sample believed that politicians were honest, it could be an example of an assimilation effect.

32
insignificant impact of spending on aggregate health status.<sup>23</sup> But their findings highlight the importance of considering the incidence of the health benefits: they benefit (at best) only the poor so cuts without reallocation would fall on the poor. As explored below the impact on the poor versus the non-poor is not a constant or immutable parameter, but depends on the composition and efficacy of public spending. Jamison et al (1996) use an unusual econometric procedure and find that public spending on health lowers mortality in their sample of Latin American countries.24 When we attempt to replicate their finding using our regressions and limiting them to only Latin America, we find a positive and significant impact of public spending on health if only income is included. However, adding our non-income control variables reduces the magnitude of the estimate by half (and it is becomes statistically insignificant)." 23 For infant mortality they find that the elasticity at the population weighted means is - 0.213 for the poor and -0.056 for the non-poor, which is roughly comparable to our -0.078 in the aggregate.

24 Jamison et al (1996) use a panel of data from 1960 to 1990 to first estimate (the natural log of) under 5 mortality as a function of income and year (and interactions) and whether the country was Latin American for a broad cross section of countries. They then use the anti-log of the predicted values to create a percentage deviation of the level of the actual and predicted for each country and then regress those percentage deviations on the public and private share of health in GDP for the sub-sample of Latin American countries.

25 The regression with just income has an R-squared of .6474 and estimates (t-statistics) for the sample of 21 countries: In(uS) = -.70S In(GNPpc) - .464 InPHS (5.3) **(3.7)** The regression which includes our non-income control variables has an R-squared of .8467 and estimates (t-statistics) for the sample of 21 countries: ln(uS) = -.759 ln(GNPpc) .247 **InPHS** + .144 FemEd + **.018** Gini - .247 Urb + .967 Ethno *.026 Trop- .004 AcSaWa (3.2) (1.2) (2.0) (.87) (1.9) (1.6) (.04) (.76) 20