rafmacalaba/datause-encoder-data huggingface dataset:
American and Canadian Rockies and coastal mountain ranges, and weather and wind patterns that vary from year to year and dictate where smoke is transported. As a result, year-to-year exposure is expected to be driven by weather conditions in locations far from our counties of interest and hence uncorrelated with weather in our county of interest. We find that burned area in California is not correlated with meteorological variables in the primary corn and soybean producing states (Table SI3). Correlations between burned area and extreme degree days for corn, soybeans, and total precipitation are all between _−_ 0 _._ 5 and 0.5 with the majority between _−_ 0 _._ 3 and 0.0.

We control for county-specific determinants of yields that are unchanging over our sample period (e.g. soil quality or elevation) with a county fixed effect. We account for time-varying determinants of yield that are common across counties (e.g. general improvements in agricultural technology) with year-specific fixed effects. We also include a range of annual county-specific meteorological controls to account for correlation between smoke exposure and weather-related determinants of yield. For example, denser smoke plumes may reduce surface-level temperatures in ways that enhance yields.<sup>16</sup> We also include county-specific controls for the fraction of cloud cover to account for the possibly confounding effect that clouds may have on the ability of analysts to accurately draw smoke plumes and the direct impact that clouds can have on yields themselves.<sup>22,5</sup> Summary statistics for all measures included in our regressions are presented in Table SI4. The full estimating equation is: where _Ln_ ( _yit_ ) represents the natural log transformation of the yield of either corn or soybeans in county _i_ in year _t_ . “Low-density smoke” and “high-density smoke” are the number of days during the growing season that each county was under a low-density or high-density smoke plume in the HMS data, averaged across corn or soy pixels. We control for the total growing degree days and extreme degree days in each county-year where the thresholds for extreme degree days varies by crop (29<sup>_◦_</sup> C for corn and 30<sup>_◦_</sup> C for soybeans). We also include quadratic controls for pre-season and growing season precipitation in each county-year. “Low cloud” and “high cloud” control for the number of days during the growing season in each county-year that have a cloud fraction between 50-75% (low) and 75-100% (high). Binning days by cloud cover allows for a non-linear response to cloud cover.<sup>22</sup> _αi_ and _χt_ are county and year fixed effects that account for county specific determinants of yield that do not vary over time (i.e. soil quality) and shocks 18
because of the still large differences in wages and among the latter because of the large benefits from agglomeration at the still mostly rich country-based centers of excellence.

# _Poor, Young and Unskilled_

The rich world is ageing while the poor world has a rising share of the young. The rich world’s public finances are in worse shape than before and less able to deal with the ageing problem in part because the ability to create wealth, from which the old could be supported, is undermined by an inadequate labor force. In other words, the problem in the rich world is that the ratio of old to young in the population is turning adverse. The poor world, on the other hand, has perhaps too many young and poor. Although the poor world is converging, growing more rapidly than in the past, it is still not able to create adequate employment opportunities for the growing restive young. The recent events in the Middle East bear out the latter proposition.

|**Age Structure in 2020 in High and Lo**|**w Income Countr**|<br>**ies**|
|---|---|---|
|_Age group_|_High-income_|_Low income_|
|Less than 19years|21.7|34.6|
|20-50|39.1|43.8|
|50-60|13.6|10.3|
|50-55|6.8|5.6|
|Greater than 55years|32.3|16.9|
|Greater than 60years|25.5|11.3|
|Total|99.9|100|
|Ratio of old to workingagepopulation I/1|48%|21%|
|Ratio of old to workingagepopulation I/2|70%|34%|
|Ratio of old to workingagepopulation I/3|100%|49%|
|1. Old defined as 60years and above|||
|2. Old defined as 55years and above|||
|3. Old defined as 50years and above|||

Source: Authors’ estimates based on UN population projections.

As the table of demographic projections above shows, regardless of how old and working ages are defined, the rich world will have many more old relative to young and vice versa in the developing world. The ratio of old to young is too great in the rich world while the ratio of old to young is arguably too small in the poor world. The conventional macroeconomic view, that capital should move from rich to poor to address the basic demographic problem was well expressed by then IMF Chief Economist, Ken Rogoff: “Isolationists in industrialised countries should stop and look at their populations' advancing age structure. As the dependency ratio 11
This paper examines _the differentiated outcomes in the Indonesian context of vocational education and academic education, particularly in terms of employment opportunities; labor market earnings; and access to tertiary education_ . It attempts to explain these differentiated outcomes by exploring whether vocational education is regarded by employers, educational institutions and other stakeholders as less prestigious and is thus stigmatized; or whether vocational education’s characteristics and quality is responsible for these differentiated outcomes. With data from a panel of two waves of “Indonesia Family Life Survey” (IFLS II and IFLS III) in 1997 and 2000, the empirical approach adopted takes into account the observation censoring problem related to disregarding students who pass through secondary education to achieve a higher level of educational attainment. Using an instrumental variable, this paper will also treat the endogeneity of household choice of vocational versus academic track of education. Both of these estimation issues have not been addressed in the existing literature on this subject so far.

# **II. Indonesian context**

# **Increasing the proportion of vocational students: a laudable and realistic goal?**

As a sub-system in Indonesia’s national education system, technical and vocational education has always played a crucial role in creating skilled human resources. At the senior secondary level, on average between one-fifth and one-quarter of enrolled students study at vocational schools ( _Sekolah Menengah Kejuruan_ , SMK), while the remainder study at general academic schools ( _Sekolah Menengah Atas_ , SMA). In recent years, the Ministry of National Education has committed itself to the expressly stated goal of _increasing the proportion of vocational school students_ to “70% vocational school students and 30% the senior high school students”<sup>2</sup> at senior secondary level.

Given the trend in recent years, it seems unlikely that this goal will be achieved. While gross enrollment at the senior secondary level has been increasing, this increase is largely due to a growth in numbers in students enrolled at general academic schools. Between 2000 and 2006,

> 2 From the forword by Bambang Sudibyo, Minister of National Education for “Sekolah Menengah Kejuruan di Indonesia”, 2006

5
situation of young women improved as their share in youth informal employment decreased from 45 percent to 42 percent between 2007 and 2009; at the same time, the share of young women among young agricultural workers remained around 47 percent.

**Table 18: Informal and agricultural employment (%)**

|||2007<br>|||2009<br>||
|---|---|---|---|---|---|---|
||Agricultural<br>employment|Informal<br>Employment|Formal<br>employment|Agricultural<br>employment|Informal<br>Employment|Formal<br>employment|
|Women|52.4|34.2|13.5|50.0|34.6|15.4|
|Men|47.9|38.1|14.1|45.4|39.9|14.7|
|**_Age_**|||||||
|15-24|51.0|36.1|12.9|47.9|36.4|15.7|
|25-34|42.7|38.4|18.9|38.4|39.0|22.5|
|35-44|47.0|39.7|13.3|43.6|42.2|14.2|
|45-54|52.6|33.6|13.8|52.2|35.3|12.5|
|55-64|65.1|28.6|6.3|64.6|30.45|4.9|
|65+|73.4|24.7|1.9|76.3|23.9|0.5|
|Total|50.1|36.1|13.8|47.6|37.3|15.1|

_Source: (LFS, 2007, 2009)_

# _Underemployment_

Underemployment complements the unemployment indicator in a country like Vietnam. It is a particularly interesting indicator: workers who lose their job and are unable to find similar employment may be forced to accept jobs that are below their expectations. In particular, they may be forced to work fewer hours than they would like.

Persons in under-employment formally consist of “ _all employed persons who during a specified period work less than 35 hours a week and who wish to work additional hours_ ” (ILO). For measurement purposes, using the LFS surveys, we use the following definition of underemployment: workers who work less than 35 hours in all jobs and want and are available to work additional hours. We also look at the hours worked in the main job, the number of jobs held, and the reasons for working less than usual.

There is evidence that a significant proportion of workers are underemployed and that underemployment is a greater issue than unemployment. The proportion of underemployed<sup>6</sup> among active workers was greater than the proportion of unemployed: while the unemployment rate was 2.6 percent, the underemployment rate was 5.4 percent.

> 6 Under-employment is defined as workers who work less than 35 hours a week in total (i.e. in all jobs) and want to work more and are ready to work more.

21
analyzed using the data collected during the program. Hygiene practices within the household may impact child’s mental development through different channels, such as nutrition, health, and high-quality supportive environments. The last channel includes mothers’ behavior on health and nutrition, as these factors make mothers more responsive with the child’s environment and his/her cognitive and socio-emotional development.

# _Potential Sources of Biases_

The randomized experiment may be biased due to contamination of the control units and the impossibility of separating the treatment effects of both arms, the national campaign versus DCC and IPC campaigns. Clearly, because multiple hygiene and sanitation campaigns were delivered to households and communities in both comparison groups, in a relatively short period of time, there could be contamination in the randomized assignment. Also there could be _ascertainment biases_ occurring when household members report outcomes that can have some sort of measurement error, such as knowledge, attitudes, and health information. Reporting knowledge about behaviors and attitudes tends to increase the ascertainment bias, which ultimately may drive part of the effect of the program. Also, _courtesy biases_ can be present because survey work and the intervention rollout made it impossible to blind participants to their treatment status; for the follow up survey the interviewers were blinded to the intervention in each community in order to minimize this potential source of bias.

# **<u>Data and Descriptive Statistics</u>**

# _Data_

The data were gathered through three different stages: _a baseline survey_ , a _longitudinal survey_ , and a _post -intervention survey_ , all of which were collected from a representative sample of the target population living in the four regions designated for the study. _The baseline survey_ was collected before the intervention and conducted from June through August 2009. The data was finally collected from about 1,150 households in 110 clusters within eligible _urban and rural communautés_ . This survey consisted of household, health, and community questionnaires, structured observations, and stool samples. _The longitudinal surveys_ were collected in three time periods before the 10
<!-- Start of picture text --> 25 Fuel import savings to GDP GDP<br>2.0<br>Q 15<br>o<br>rc}aa<br>2 10<br>0.5<br>0.02222 24 = 26 28 30 32 34 36 38 40<br>+2e3<br><!-- End of picture text --> <!-- Start of picture text --> 25 Cumulative import savings to GDP GDP<br>20<br>Q15<br>So<br>ic]<br>a<br>2 0<br>5<br>—t2222 24 = 26 28 30 32 34 36 38 40<br>+2e3<br><!-- End of picture text --> <!-- Start of picture text --> 25 Fuel import savings to GDP GDP 25 Cumulative import savings to GDP GDP<br>2.0 20<br>Q 15 Q15<br>o So<br>NN<br>a<br>ic]<br>rc}aa<br>2 10 2 0<br>0.5 5<br>0.02222 24 = 26 28 30 32 34 36 38 40 —t2222 24 = 26 28 30 32 34 36 38 40<br>+2e3 +2e3<br>GDP<br>6 8 Debt (%GDP)<br>|g TFP EM Carbon GETFPI Carbon| |<br>oe | 2 6<br>£4g3 q 3e35 ||<br>=§:3 £>4 |<br>3, ov<br>z | ieda |<br>1<br>1<br>0 0<br>22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40<br>os Current Account Balance (%GDP), with fuel savings 0.0 Current Account Balance (%GDP), without fuel savings<br>|gooTFP HB Carbon -0.5<br>0.0<br>2 ¢ -1.0<br>g 3<br>8-05} B-15<br>EE<br>££ -2.0<br>>><br>g-1.0 BS -2.5/ }<br>2 2 |<br>zg 2 -3.0<br>-15-2.022 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 -3.5-4.022 GE23 24TFP25 26 27EB28 Carbon29 30 31| 32 3334 35 36 37——~38 39 40|<br><!-- End of picture text -->
11

# **3 Climate and economic incentives drive PHL**

The proximate drivers of PHL have been well identified in the agronomic literature.<sup>17</sup> In its most elementary form, (on-farm) grain PHL depends on the storage depletion pattern—the faster stocks are depleted, the lower is the proportion lost. This depends in turn on-farmers’ marketing and auto-consumption behavior.<sup>18</sup> With most cereal sales typically taking place shortly after the harvest<sup>19</sup> given credit or liquidity constraints (Stephens and Barrett,2011), higher degrees of commercialization are expected to lower grain PHL given shorter storage time (Hodges, 2010). But, such savings may be offset by losses during marketing (due to transport, handling and sorting). Marketing losses are avoided if cereals are mainly stored for auto-consumption, though this comes with more gradual stock depletion and thus higher risks of losses due to pest, insect infestation and/or rodents as time goes by. Whether storage for marketing or auto-consumption leads to higher or lower PHL is ultimately an empirical matter.

PHL may also increase with higher production quantities, for example, when this creates labor shortages and less attention to harvest conservation, or indirectly through lengthier storage duration, so as to benefit from inter-temporal arbitrage on markets, or through lower general equilibrium price levels when there is overall a bumper crop, and thus lower gains from loss prevention (World Bank, 2011b). Everything else equal, PHL is further expected to decrease with the use of improved storage technologies and crop protection and to increase with humidity and temperature, with hotter, more humid environments fostering pest infestations and rotting.

> 17 For a critical review of grain PHL drivers and systems, see Greeley (1982; 1986), Boxall (1986; 2001), or Grolleaud (1997).

> 18 Henkes (1992) for example found that over a 9-month storage period, cumulated maize PHL in a number of small farms in Tanzania was 2/3 lower when maize had been stored for auto-consumption than when depletion followed purely from commercialization (two extreme cases). However this study might not depict a representative picture of the reality when marketing is concentrated in the first three months after harvest rather than being evenly spread over time.

> 19 For example, in the sample surveys analyzed here, between 84 and 91 percent of the total share of maize marketed, had been marketed within 3 months. This compares with an average storage duration of 5.5-7 months.
**Decoupling and recoupling**

Have the betas of EM assets to global assets come down in recent years? This is readily illustrated by a back-of-the-envelope estimation of betas relative to standard global proxies for each of the relevant markets. Specifically, we run country-by-country regressions of monthly log changes in MSCI country equity indexes, exchange rates and credit spreads on log changes in the S&P 500, Broad Dollar Index, and US, respectively, to estimate alphas and betas as the ordinate and the coefficient from the regressions.<sup>10</sup> We focus on two time periods: early (2000-2004) and late (2005-2010), and we split the latter into a tranquil period (2005-2008M02) and a longer late period including the crisis (2005-2010). We replicate the exercise for quarterly and annual changes to examine whether longer time horizons enhance the effect of fundamentals at the expense of global markets.

As **Figure 10** clearly shows, betas have remained persistently high in the second half of the 2000s, even as we lower the sampling frequency to allow for short-term co-movement to dissipate ( **Table 6** report the group means and medians). Equities betas have remained close to 1, while credit betas (sovereign bond spreads vis a vis high yield corporate spreads in advance countries) and exchange rate betas have generally increased. **Table 6** also shows that high betas are not a result of the global financial crisis, as the pre-crisis period (2005-2008M02) presents betas in EM’s assets that are greater or equal to betas during the early 2000s. Interestingly, this change in global betas is not idiosyncratic to EM, as it also applies to the advanced economies in our sample. However, financial recoupling in these economies have less strength than in EM, and is particularly found in equity markets and to a less extent in FX markets.<sup>11</sup> To further investigate financial recoupling we performed panel estimations instead of country-by-country regressions of log changes in EM assets against global assets. Results are summarized in **Table 7** . For the three assets under consideration, we observe financial recoupling during the late 2000s even when we remove the crisis period (2008M03-2009M03).<sup>12</sup> Our results are also in line with Dooley and Hutchinson (2009). In their study, they conclude that emerging markets experienced greater financial transmission during the worst period of the

> 10 We use the S&P, Broad Dollar Index and US HY instead of broader global indexes to be able to estimate alphas and betas for the advanced markets we used for comparison in the previous piece of this series. Estimating equity betas to the global MSCI yields comparable results. We choose MSCI equity indexes to local stock market indexes to concentrate on the more liquid, globally traded stocks used in cross-border operations.

> 11 Sovereign debt in advanced economies is largely domestic and denominated in local currency, hence not directly comparable with EM credit.

> 12 Financial recoupling is also present with semi-annual and annual log changes. These results are not presented for brevity but are available upon request.

<u>9</u>
**Table 5: Investment in mutual fund Y (Increasing risk to Islamic fund)**

||Dependent V|ariable: Inve|stment in alte|rnate (Islami|c) fund|||
|---|---|---|---|---|---|---|---|
||I<br>Baseline|II<br>Controls|III<br>Decision<br>interaction|IV<br>Risk<br>preference<br>interaction|V<br>Religious<br>preference<br>interaction|VI<br>All<br>interactions|VII<br>Triple<br>interaction|
|Treatment (Islamic fund = 1)|204.5*|220.1*|21.9|58.0|35.2|-322.5|-132.4|
||(114.7)|(117.5)|(148.2)|(398.5)|(158.7)|(419.1)|(428.4)|
|Decision number (1 - 12)|-178.3***|-178.3***|-193.6***|-178.3***|-178.3***|-193.6***|-192.0***|
||(7.0)|(7.0)|(9.8)|(7.0)|(7.0)|(9.8)|(14.1)|
|Risk preferences||-100.5|-100.5|-128.0|-95.33|-122.3|-122.3|
|7 = Risk-seeking||(64.20)|(64.20)|(91.07)|(63.72)|(90.35)|(90.35)|
|Religious preferences||1.399**|1.399**|1.408**|0.577|0.586|0.677|
|Donation to mosque||(0.60)|(0.60)|(0.60)|(0.76)|(0.77)|(0.97)|
|Treatment X|||30.51**|||30.51**|1.270|
|Decision number|||(13.89)|||(13.89)|(19.52)|
|Treatment X||||55.5||54.7|54.7|
|Risk preference||||(130.3)||(129.2)|(129.2)|
|Treatment X|||||2.048*|2.046*|-0.252|
|Religiouspreference|||||(1.20)|(1.20)|(1.53)|
|Religious preference X|||||||-0.014|
|Decision number|||||||(0.09)|
|Treatment X|||||||0.354**|
|Religiouspreference X D|ecision number||||||(0.1)|
|Constant|2955.3***|3221.7***|3320.8***|3252.2***|3053.3***|3182.7***|3172.5***|
||(92.84)|(1112.20)|(1113.10)|(1118.60)|(1106.90)|(1114.30)|(1116.20)|
|Additional Controls|NO|YES|YES|YES|YES|YES|YES|
|Observations|1464|1464|1464|1464|1464|1464|1464|

Notes: Random effects panel (GLS) regressions. Dependent variable is amount (in tokens) invested in mutual fund Y. * 10%, ** 5%, *** 1% significance level. Additional controls include gender, age, financial literacy, investing experience, confidence that the charity was paid in line with instructions, and clarity of instructions (see table A.2 in the appendix B). Religious preferences are measured in tokens, with a maximum of 400 tokens. Risk preferences are measured based on the number of times a subject selected the gamble in the risk preference measure. Financial literacy is measured by the number of correct answers the subject provided in the financial literacy questionnaire. Appendix A defines the main variables.
36

**Table SI-10:** Impact of GDP and storms on net migration

||Full sample|Below median GDP|Above median GDP|
|---|---|---|---|
|2019 GDP (MM)|0.124<sup>_∗∗∗_</sup>|3.971<sup>_∗∗∗_</sup>|0.100<sup>_∗∗∗_</sup>|
||(0.038)|(1.085)|(0.028)|
|Total storms|0.500<sup>_∗∗∗_</sup>|-0.032|0.251<sup>_∗_</sup>|
||(0.143)|(0.079)|(0.131)|
|N|2,332|1,133|1,199|
|Impact of 1SD change in GDP|2.772|88.991|2.237|
|Impact of 1SD change in storms|1.383|-0.088|0.694|
|Fixed Efects:||||
|State|Yes|Yes|Yes|

Notes: All columns report the results of a fixed effects specification with the IHS transformation of the number of net-migrants over our sample period from a county as the outcome. Net migration is defined as in-migration minus out-migration so that negative net migration indicates a declining population. Total storms is the sum of the storms the county is impacted by in our sample.<sup>_∗_</sup> p=0.1,<sup>_∗∗_</sup> p=0.05,<sup>_∗∗∗_</sup> p=0.01.