rafmacalaba/data-use-mentions-extended huggingface dataset:
tively higher safety, rapidity, and cost-effectiveness (Puri, 2005). They can be useful in a wide range of monitoring and planning operations, including incident response, traffic surveillance on roadways and intersections, parking lot monitoring, origin-destination flow estimation, among other applications (Coifman et al., 2004; Puri, 2005). Like call detail records from mobile phones, drones are increasingly filling data gaps inherent to scarce or infrequently collected travel surveys.

# B. Human and Social Sensing

In the digital era, virtual interactions on social media generate content or data (referred to as user generated content or UGC) on real-time trip experience, traffic conditions, and travel time unreliability. Attempts to characterize travel patterns or detect traffic anomalies, for instance, are relying on smartphone-compatible platforms such as Twitter, Foursquare, Instagram, and other social media. A good example of this comes from He et al. (2013) who examine the predictive power of travelers’ social media activities in San Francisco and find a negative correlation between the intensity of tweets and traffic volume. More importantly, while most existing models have a short-term forecasting horizon, these authors show that tweet-based semantics can be exploited to significantly improve longer-term traffic predictions. In the same vein, self-reported positions on Foursquare and Instagram (Ribeiro et al., 2014), and traffic complaints on Twitter (Georgiou et al., 2015) have also been shown to be highly correlated with traffic congestion.

The sources of traffic obstruction can be analyzed using social sensing data describing commuters’ reactions to real-time disruptive events. To do this, a social sensing software scrutinizes a given social media platform, looking for keywords such as “traffic”, “congestion”, “accident”, and “roadwork”, and extract useful information for real-time traffic analysis. For example, Pan et al. (2013) combine human mobility data from GPS tracers with social media data from Weibo (a Twitter-like social media in China) to characterize 4
more effective at increasing the employability of youth and promoting youth‐owned household enterprises.

The absence of statistically significant differences across the groups may partly reflect the evaluation timeline and the consequences of the Ebola crisis. The endline survey was collected less than a year after the end of the intervention, thus covering a short span postintervention. As a result, the market may not have yet recognized the newly acquired skills and abilities, limiting comparisons across the groups. This drawback was intensified by the fact that program participants graduated from the training to join a labor market severely affected by the Ebola crisis. The ability of the participants to secure employment was undoubtedly affected by the generally poor economic climate, as evidenced by the steep decline in the urban employment rate from 75 percent to 67 percent, an 8 percentage point increase in the number of household enterprises out of operation (a measure of business survival), and a contraction in business revenues by 40 percent (Fu et al. 2015). However, the lack of differences also provides suggestive evidence that foundational skills, particularly basic literacy and numeracy, and participation in an intensive training intervention alone have the potential to enhance skills among youth in generalized low‐skill contexts.

# _Cash versus skills_

There is some debate as to whether skills or capital are more important at inducing changes. The study design did not allow for a robust comparison between these two elements, making it difficult to disentangle the respective effects. In addition, although the program was designed to reduce capital constraints to entrepreneurship through microfinance facilitation, the Ebola crisis disrupted this aspect. The program’s influence in relieving capital constraints was thus reduced to a cash infusion in the form of the stipend provided to all groups. Nonetheless, the data allow some reasonable speculation that neither skills nor cash alone (at least in the amount provided) would have been sufficient to achieve the range of impacts found.

19
firmlevel ESG-related information, as the full assessment of each company is not possible. This is different from our methodology, which is entirely based on firm-level information of the Enterprise Survey and does not leverage any country data, despite the availability of ESG country data for numerous emerging countries (World Bank, 2021).

The rating methodologies provided by the main ESG rating agencies differ from each other significantly (in terms of building blocks, materiality assessment, weights used to aggregate the information, etc.) and cannot be reconciled under one single approach. The main building blocks utilized by the ESG rating agencies are typically the following (sometimes similar concepts appear under different names):

- Environmental:

- Environmental strategy or green management

- Environmental impact or footprint

- Social

- Gender

- Human resources, including education and training

- Labor conditions

- Community involvement

- Governance

- Corporate governance and stakeholders

with oekom research in 2018; Sustainalytics, which is part of Morningstar; MSCI ESG Rating (which in 2019 acquired Carbon Delta, a Zurich-based environmental fintech and data analytics firm specializing in climate change scenario analysis).

4
# **_2.2 Financial literacy, education, and use of financial services_**

Previous studies have examined the relationship between greater financial literacy and financial education and use of financial services. Multiple studies have found that people with more years of general education are more likely to save and invest, leading to higher financial market participation and lower probabilities of bankruptcy (for example, Cole et al. 2014). Furthermore, a recent meta-analysis of the evidence highlights the effectiveness of financial education on financial knowledge and behavior (Kaiser et al. 2022). Surveys measuring levels of financial literacy have also shown relationships with usage of financial services and savings and borrowing (Klapper and Lusardi 2020; OECD 2020).

It could also be possible that account usage and the experience with using an account can improve financial literacy. Cross-country analysis finds higher financial literacy to be associated with greater financial inclusion as measured by account ownership, debit card ownership, using an account to save, and using a debit card in the past year at the country level (Grohmann et al. 2018). For instance, “learning by doing” financial literacy training conducted while opening an account was associated with more frequent and savvy account usage (Breza, et al. 2020; Lee et al. 2021). Presenting customers with simplified key facts about financial products likewise was associated with better product selection (Giné et al. 2017). In fact, the use of digital payment tools and platforms was found to be associated with higher digital literacy, at all levels of financial literacy (Lo Prete 2022). Additionally, easily available real-time information on account balances can increase user trust and account usage (Bachas et al. 2021).

6
|-0.398***|
||(-12.46)|(-3.229)|(-7.646)|(-4.632)|(-12.77)|(-5.363)|
|Palm|17.51***|32.71***|15.72***|29.96***|19.73***|28.83***|
||(16.28)|(17.57)|(14.10)|(15.41)|(13.08)|(10.59)|
|Intercept|20.88***|10.35***|16.96***|28.44***|21.29***|10.08***|
||(22.24)|(3.712)|(10.50)|(4.618)|(20.82)|(3.325)|
|Observations|1,932|1,932|1,374|1,374|1,312|1,312|
|Year and Quarter dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Ramadan Dummies|No|Yes|No|Yes|No|Yes|
|Relative Prices|No|Yes|No|Yes|No|Yes|
|Fuel Price|No|Yes|No|Yes|No|Yes|
|Dummyif World Price rising|No|Yes|No|Yes|No|Yes|
# **1. Introduction**

In the mid-1980s, Indonesia shifted its development strategy focus from import substitution to export orientation. This transition was accompanied by deregulation aimed to better link Indonesia with the global economy (Saryahadi, 2001). During the 1990s, the country’s employment and trade patterns evolved, with agricultural employment decreasing in relative importance and trade partners such as China and India emerging as important destinations.

In this paper, we assess the degree to which changes in exports affect labor markets in Indonesia. We estimate how exports affect a range of local labor outcomes, including wages, employment, informality, and labor force participation. Up to six years after an initial shock, we calculate Impulse Response Functions (IRFs) that capture the evolving short- and long-run impacts of export exposure on labor outcomes.

The primary analysis is based on a dataset that combines the Indonesian labor force survey (Sakernas) and industry exports (UN Comtrade) spanning from 1993 to 2014.<sup>2</sup> To address the potential endogeneity of exports (the fact that exports are potentially correlated with unobserved characteristics of firms/regions), we isolate the exogenous part of exports using foreign demand shocks of Indonesian trade partners. Intuitively, this strategy leverages the fact that each local market in Indonesia is a small open economy; that is, they are engaged in international trade but are small relative to the world market and must take prices and global demand as given rather than trying to influence them strategically. Therefore, each region is exposed to global demand shocks, which are likely uncorrelated with the distribution of unobserved characteristics across the Indonesian districts.

The findings suggest that employment increases, the labor force expands, and unemployment declines in districts that experience an increase in exposure to foreign demand. More specifically, on average, the elasticity of employment with respect to exports is 0.1 over the short run and 0.3 over the long run. The long-run elasticity of the labor force with respect to foreign demand
measures of soil quality, and the nature of farm organization as captured in the IHS3 data does not allow us to feature household fixed effects as a central piece of our empirical strategy. The average number of plots cultivated by Malawian farming households is 1.76, significantly less than the comparable statistics from West African settings that have largely informed the analysis of the gender gap in sub-Saharan Africa thus far. The managers identified across agricultural plots cultivated by a given household also correspond to the head of household in an overwhelming sample of households that report to be cultivating multiple plots. Specifically, there are only 109 households that cultivate multiple plots and exhibit within-household variation in terms of the gender of the plot managers, corresponding to 1.7 percent of our plot sample. Nevertheless, we attempt to lend as much support to the overlapping support, ignorability, and zero conditional mean assumptions as possible by relying on all available data and econometric methods at our disposal. These sensitivity analyses are presented later in Section 5.3.

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51% 50%<br>services<br>Agriculture, hunting,<br>forestry, and fishing<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> Source: Authors’ calculations with data from Sakernas.

**Merchandise trade was, and continued to be, dominated by manufacturing.** Mining and quarrying account for about 20 percent of total exports value with the primary agriculture sector counting for less than 10 percent of the total value of exports. Some of the most important export products are coal briquettes (10.9% of total value), palm oil (8.21%), petroleum gas (4.48%), gold (2.16%), and rubber (2.07%).

**Indonesia’s six largest trade partners have persistently accounted for at least 60 percent of total exports value.** However, the relative relevance of each partner has changed substantially over time. Japan accounted for nearly one-third of exports by value in the beginning of the period and less than 10 percent by the end. Conversely, China grew in relative relevance from less than 5 percent to more than 15 percent of export values.

4
targeting formulas.

By design the PPMT cannot reproduce the score that would be produced by the full PMT model. Therefore, we can only assess the probability that a case with a certain PPMT score will be eligible for the MoLSA’s program determined using the full PMT model. Since both the PPMT and the full PMT for MoLSA’s program are devised using the same national household survey, the 2017/18 Survey of WellBeing via Instant and Frequent Tracking (SWIFT), we use the data to calculate the probability of qualification under the full PMT given a household’s PPMT score. Households with lower PPMT score will have higher probability of being eligible under the full PMT and vice-versa. One can be conservative in making the referral recommendation, i.e., referring a caseload to MoLSA only if its PPMT score is very low so that its probability of selection for the MoLSA program is very high. The benefit of doing so is a high “hit” rate, but it may be costly in that many cases that would be eligible are not referred. On the other hand, one could be liberal in making the referral recommendation by increasing the PPMT score threshold below which cases will be recommended to MoLSA’s review. This reduces the chances that potentially eligible cases do not get an opportunity for MoLSA’s screening because they are not referred. However, it will add significant administrative burden on MoLSA as many cases will be referred for verification.

Results from applying the pseudo-PMT models to the CLCI’s legacy database of current and past beneficiaries (37,457 households corresponding to 224,736 individual) suggest potential for both significant referral numbers and a sequenced referral strategy.<sup>5</sup> The analysis suggests that referring only those in the bottom 18 percent nationally (the national poverty rate according to SWIFT 2017/18 survey

> 4 Throughout the paper, Social safety net (SSN), Social Protection Network (SPN), national program, MoLSA’s cash transfer program
highlighted in the data by comparing yields across different plots of the same farm. Comparing plots of the same farm should hold many of the aforementioned factors constant. However, as Figure 5 emphasizes, there is considerable heterogeneity in yields even within the same farm and even when only regarding pure-stand plots without losses or comparing crop cut yields on plots without losses.

# <u>What do we conclude and still need to know?</u>

These considerations highlight the difficulties in finding a valid counterfactual based solely on realized yields, either exploiting the time or cross-sectional dimension of survey data. Using past harvest observations as a proxy for current season attainable harvest requires panel data of higher frequency than is common in current agricultural surveys and would not capture any households that have changed location or entered the panel. Fundamentally though, they would provide an estimate of attainable harvest that is downward biased to the degree that shocks have also occurred in previous survey rounds, a likely scenario based on our sample, and be prone to bias from intertemporal differences in plot management decisions. Furthermore, the large inter-farm variability in yields due to factors other than shocks that is reported in the literature and heterogeneity in yields across plots of the same farm in our data cast doubt on the suitability of using realized yields from other farms to proxy for the attainable harvest.

However, using rich survey data as is provided by the LSMS-ISA, it may be possible to account for possible confounding factors and predict (attainable) yields using econometric or machine learning models based on past yield data, data from several similar but unaffected plots, and/or planting season crop management decisions. An exploration of this possibility is beyond the scope of this paper but may be a relevant avenue for future research.

# ’ - <u>Question 5: Can farmers post planting harvest expectations proxy for attainable harvest?</u>

As previously discussed, the LSMS-ISA data contains detailed information on plot, input, and management characteristics as well as agricultural production down to the plot-crop
the relevant cycle’s CN files. Agriculture committee and sub-committee assignments for each Congress were obtained from Congressional Quarterly (1996-1999).

41 Top 20 list-makers who were not on the Agricultural committee represented districts with influential agricultural constituents (CA, OK, VA, MI). For example, Fazio’s district (CA, district 3) was among the 30 leading districts by market value of agricultural products (1997 Census of Agriculture).

> 42 These include payments for acreage reductions, commodity credit loans, payments for meeting environmental targets satchel wetlands, etc.

17
expenditures in rural education, health, and other social sectors. All in all, rural public expenditures are disaggregated into 12 categories. The expenditure groups (including in brackets their respective average country share in total public expenditures for the year 1995) are the following: marketing assistance (28), irrigation (7.7), technological generation and transfers (4.7), soil conservation (1.2), forest promotion (0.09), plant and animal sanitation (4.2), communications and information (1.6), “focalized” expenditures directed to particular commodities (4.9), integral rural development (IRD) (4.1), rural public infrastructure (16.3), rural education, health, and other social services (25.5), and others (1.7).

To allocate the above expenditures into private versus public goods we first account for items that clearly fall into one or the other category. Next we deal with those categories that fall into a gray area where it is difficult to determine a priori whether they correspond to public or private goods. In these cases we have looked into more detailed components falling into these categories in the various country statistics available. This allowed us to obtain an educated guess as to what proportion of these expenditure items should be approximately allocated to public or private goods. Finally there are some minor items for which we were not even able to have a reasonable guess. In the latter cases we simply use a 50-50 rule.

Among the items that clearly qualify as public goods are technology generation and transfers, soil conservation, plant and animal sanitary protection, communications and information services, rural roads, and social services (public education, health, and anti-poverty programs). The above expenditures were classified as public goods, because they increase the supply of services that generate important positive externalities (technology generation and diffusion, soil conservation, plant and animal sanitation), were expenditures directed to at
**Table 2:** _List of Countries in the Sample and Information on DHS Data_

|HIPCs|DHS|Non-HIPCs|DHS|
|---|---|---|---|
|Benin|2011-2012|Albania|2008-2009|
|Bolivia|2008|Angola|2011*|
|Burkina Faso|2010|Armenia|2010|
|Burundi|2010|Azerbaijan|2006|
|Cameroon|2011|Bangladesh|2011|
|Chad|2004|Cambodia|2010|
|Comoros|2012|Colombia|2010|
|Democratic Republic of Congo|2013-2014|Dominican Republic|2013|
|Republic of Congo|2011-2012|Gabon|2012|
|Cˆote d’Ivoire|2011-2012|Kenya|2008-2009|
|Ethiopia|2011|Kyrgyz Republic|2012|
|The Gambia|2013|Laos|2011-2012<sup>†</sup>|
|Ghana|2014|Lesotho|2009|
|Guinea|2012|Maldives|2009|
|Guyana|2009|Moldova|2005|
|Haiti|2012|Namibia|2013|
|Honduras|2011-2012|Nepal|2011|
|Liberia|2013|Nigeria|2013|
|Madagascar|2008-2009|Pakistan|2012-2013|
|Malawi|2010|Peru|2012<sup>#</sup>|
|Mali|2012-2013|Swaziland|2006-2007|
|Mozambique|2011|Tajikistan|2012|
|Niger|2012|Timor-Leste|2009-2010|
|Rwanda|2010|Yemen|2013|
|S ̃ao Tom ́e and Pr ́ıncipe|2008-2009|Zimbabwe|2010-2011|
|<br>Senegal|2014<sup>#</sup>|||
|Sierra Leone|2013|||
|Tanzania|2010|||
|Togo|2013-2014|||
|Uganda|2011|||
|Zambia|2013-2014|||

*Malaria Indicator Survey (MIS) from the DHS Program #Continuous DHS †Joint Unicef Multiple Indicator Cluster Survey (MICS
that, in poor settings, individuals have come to treat sickness and doctor‘s visits as a regular part of their daily lives rather than an extraordinary event.

Figure 1 illustrates this effect in the observational data. The figure plots the number of acute illnesses reported in the second period of data collection on acute illnesses as reported weekly in the first period. The second period data is divided into weekly and monthly recall period. Clearly weekly recall is higher than monthly. Further, the higher the number of illnesses in the initial year, the higher the number of illnesses and the larger the gap between monthly and weekly reports is in the next period. Interpreting this gap as ―forgetting,‖ the results suggest that forgetting occurs in strict proportionality with the number of health episodes, a result that even extends into the unreliable area of the data where observations are sparse.

Finally, Table 5 presents a robustness check on our results to account for missing observations. Specifically, in the weekly survey, surveyors were instructed to visit every household on the same day every week to ensure that the reference period was benchmarked to the previous visit. One of the repercussions of this rigid survey schedule was that 15 percent of our weekly observations were missing. In around half of these cases, the observation is missing because the surveyor did not visit the household due to a national holiday and in the remaining half, it is missing because the house was locked or the respondent could not be found despite repeated visits during the day. These missing variables create a particular problem when we aggregate weekly reports into monthly data since in the monthly recall surveys, a week outside the house would not result in a missing observation. In our previous regression specifications, we took the extreme position that the missing variables correspond to a fully healthy report for the individual in the missing week—this ensured that we computed a lower bound for the recall effect. In Table 5, we present estimates from two alternate assumptions—that the data were ―missing at random