rafmacalaba/data-use-mentions-extended huggingface dataset:
# TABLE 2 Data employed in the panel regression

|Data|Definition|Source|
|---|---|---|
|Trade costs|Logarithm of the geometric average of<br>country i's and j's bilateral trade costs|UNESCAP-World Bank<br>Trade Costs Database|
|Tariff rates|Logarithm of the geometric average of<br>country i's and j's bilateral tariff rates|UNESCAP-World Bank<br>Trade Costs Database|
|Regional Trade<br>Agreements (RTA)|Dummy variable equal to unity if<br>countries i and j share a common RTA|CEPII|
|Common border|Dummy variable equal to unity if<br>countries i and j share a common land<br>border (adjacency).|CEPII|
|Common language|Dummy variable equal to unity if<br>countries i and j share a common<br>language|CEPII|
|Distance|Logarithm of distance (in kilometers)<br>between the largest cities in two<br>countries|CEPII|
|Logistic Performance<br>Index|Logarithm of the geometric average of<br>country i's and j's scores|World Bank|
|Liner Shipping<br>Connectivity Index|Logarithm of the geometric average of<br>country i's and j's scores|World Bank|
|Trade policy uncertainty|Logarithm of the geometric average of<br>the country_i_'s and_j_'s gap between<br>bounded and applied tariff rates|World Development<br>Indicators Database|

Source: CEPII; United Nations Conference on Trade and Development; World Bank.
population among the controls. Columns (1-3) report the estimates from a linear model using the _log_ number of cases, while columns (4-6) report the results from a Poisson regression. In both cases, the results are closely aligned with the evidence presented so far, indicating that the campaign had beneficial effects in urban localities only. For example, the _log_ specification estimates a 30% reduction in urban incidence, and the effect is even larger in the Poisson model (both estimates are significant at the 95% confidence level).

# **6 Mechanisms: Limited Effectiveness or Limited Reach?**

The analysis of survey and health facility data indicates that the MNM campaign benefited households residing in solid dwellings and in urban settlements. However, it did not benefit households that are most susceptible to malaria. What mechanism underlies these differential effects? There are two potential explanations for our results. First, the ad content itself may not have been effective for the sub-population residing in non-solid dwellings and rural areas. This could indicate shortcomings in the identification and ad design for these type of personas. Second, the ad campaign might not have sufficiently reached this key sub-population. This underscores the importance of accounting for algorithmic targeting (Lambrecht and Tucker 2019; Sapiezynski et al. 2022) when aiming to engage with underrepresented, geographically dispersed (and therefore more expensive to reach) populations on social media platforms. To disentangle these potential mechanisms, we employed two additional analyses. First, in a post-campaign survey we included ad-recall questions to assess how well participants remembered the campaign messages. Second, we implemented a _feed_ experiment where we ensured exposure to the campaign for both household types. The results of these analyses are detailed below.

## **6.1 Post-Campaign Recall**

Our post-campaign survey measured ad recall for the MNM campaign. If the ads reached both solid and non-solid dwellers at similar frequencies, then one would expect both audiences to equally recall the Facebook advertising or the advertiser itself. To measure this, in the
establishment, the dynamism of Zambia’s large and medium farm sector and the extent of land market functioning are clearly limited.

While listing survey data provides information on the amount of land owned by farms greater than 5 ha, translating this into a share of the total requires an estimate of the area cultivated by small farmers. To get such an estimate, we use micro data from the 2014/15 post-harvest survey by the Zambia Statistical Agency which puts the total area cultivated by farmers below 5 ha in Eastern Province at 502,852 ha (with the 95% confidence interval between 481,529 and 524,174 ha). Based on table 1, the total area owned by large farms in the province amounts to 175,753.<sup>23</sup> Survey evidence suggests farmers below 5 ha cultivate virtually all the land they own (Ali _et al._ 2020) compared to between a third or about half of the area (measured by imagery or farmers’ own estimate) for farms greater than 5 ha. This would imply that large farms account for at most 26% of the total area owned by farmers in Eastern Province, though only about 12% of cultivated area,<sup>24</sup> an estimate of owned area well below the more than 50% of land that recent studies suggest is held by farms in the 5-100 ha group nationally by (Jayne _et al._ 2014a). If these estimates are correct, large farms would need to hold well above 50% of agricultural land in other provinces.

# **3.2 Linking census data to administrative records**

As official cadastral data include spatial information only for a subset of the properties located in Lusaka and neighboring provinces, comparing _de facto_ property rights (i.e., area occupied) to _de jure_ ones (sketch maps or survey plans attached to official deeds) requires digitization of survey plans and sketch maps. Doing so suggests a total area under digitized parcels for
margin, wage endogeneity and measurement error can lead to downward biased estimates. Our participation and earnings data come from administrative records and are thus less subject to measurement error. In addition, while our participation elasticities are somewhat lower than those in Stafford (2015), survey data can explain the differences in estimates. In particular, boat owners in our sample are the main income earner in the household and their spouses make money selling the fish that their husbands are unable to sell to merchants in the local fish market. Boat owners thus behave more like single individuals than married individuals, which could explain the lower elasticities that we find.<sup>10</sup> Unlike Stafford (2015), our analysis includes the possibility of

> 8Farber (2005) argued that the specification of Camerer et al. (1997) suffered from downward division bias because log hours worked on a given day were regressed on log hourly wage, computed by dividing daily earnings by the reported number of hours worked that day. Oettinger (1999) argued that daily wage fluctuations in Camerer et al. (1997) might also have been driven by changes in total labor supply of taxi drivers, hence, leading to inconsistent estimates of the labor supply elasticity due to the wage endogeneity. 9Other papers that have tested the hypothesis of reference-dependent preferences are, Abeler et al. (2011); Crawford and Meng (2011); Fehr and Goette (2007).

> 10The literature suggests that labor elasticities are larger for married individuals than single individuals, presumably because household bargaining allows couples to take advantage of temporary increases in the wage rate.

4
mentors in Chiapas, and given these constraints, mentors are allocated across communities on a rotating basis.

# **6.1 The Exposure Effect of the Program**

We start by analyzing the initial transition of the schools in the evaluation sample from the experimental situation to the policy at scale. We do this by estimating the following regression model: where 1 � _ExpPlusj_ ( _i_ ) = _k_ � _∈{_ 0 _,_ 1 _}_ represents an indicator variable for whether school _j_ is exposed to _k ∈{_ 0 _,_ 1 _, . . . , K}_ years of API _Plus_ program, while **_X_** _j_<sup>_Criteria_</sup> ( _i_ ) is a vector of indicator variables for the program eligibility criteria. Our outcome of interest ( _Yj_ ) represents the 2016–2017 school-level transition rates to seventh grade. This variable is constructed from the same administrative source we used for our experimental evaluation. The sample includes 207 schools of the 224 that were part of the experiment. Beyond a school that permanently closed, the sample attrition is caused by schools not having sixth graders during that school year. This fact is consistent with the multi-grade nature of the CONAFE system. Attrition is balanced with respect to the total years of exposure to API _Plus_ ( _p_ -values = 0.467, 0.812, and 0.568).

We exploit the change in situation to analyze the exposure effect to the program one year after the government rollout, when school closures were still minimal (only one school closed among our 224 schools by the fall of 2017). After the first year of the rollout, schools started to close more intensively (11 additional schools closed during the second year); hence, outcome variables based on survey or administrative school data during this period would suffer from endogenous censoring.<sup>18</sup> By the spring of 2017, the total years of exposure to API _Plus_ range
In the latest Global Competitiveness Report, for instance, Singapore was ranked 25<sup>th</sup> in terms of firm-level innovation in 2002, way below most developed economies at a similar level of per capita GDP.<sup>10</sup> Singapore ranked highly (top 5-10 in the world) in terms of technologyusing indicators such as quality of school science and technology education, licensing of foreign technologies etc, but was rated much lower in technology-creating indicators like R&D spending, R&D personnel, availability of venture capital and intellectual property protection (10th-17th in the world). At the same time, Singapore ranks poorly in terms of entrepreneurial activities. In the 2003 Global Entrepreneurship Monitor studies, Singapore ranked 21<sup>st</sup> among the 31 countries surveyed in total entrepreneurial activity. Within the subset of 22 OECD/East Asian countries, Singapore stood at a dismal 15<sup>th</sup> .<sup>11</sup> A new strategy to develop Singapore’s comparative advantage in innovation began to emerge from 2000. The central element of the new policy is the holistic approach it assumes. In addition to applied, technological innovations in the manufacturing sector, there is now also a strong emphasis on innovation in the services sector, including the “creative industries”<sup>12</sup> and entrepreneurial activities. Moreover, policy incentives are extended not only to big firms, but also small and start-up companies. Supporting infrastructure, both physical and institutional, are being developed in this connection. There are also efforts to change the “mindset” of Singaporeans, to bring out the enterprising and adventurous spirit in them. In short, the new strategy calls for an aggressive build-up of what some economists call “innovation infrastructure” (as opposed to “efficiency infrastructure”).<sup>13</sup> The new strategy reflects the city’s ambition to redefine its “premium” over other economies
in theoretical and actual transfers _within_ municipalities.

# **4 Empirical results**

## **4.1 Validity tests**

One potential concern about the validity of the fuzzy RD design we adopt is potential _−_ manipulation of the running variable for example, if local governments were able to attract more inhabitants to obtain larger transfers, or manipulate the IBGE population estimates sent to the TCU. As we noted above, the way in which IBGE population estimates are constructed makes these sources of potential manipulation unlikely. Even if the official population figures released to obtain the transfers were subject to manipulation, the use of IBGE estimates (rather than TCU data) as an instrument would remove this problem.

We nevertheless inspect for manipulative sorting by formally testing for the presence of discontinuities in the density of IBGE population estimates at the population thresholds we use in our empirical analysis. In line with McCrary (2008), we test for the existence of a density discontinuity at these thresholds by running kernel regressions of the log of density separately on each side of the threshold. We consider all seven thresholds pooled, and also examine the four initial ones separately (for which we have a larger number of observations in the vicinity of each threshold).<sup>10</sup> The results are reported in Figure 2 and do not show evidence that municipalities sort just above the FPM thresholds.

In addition, we perform balance tests on several time-invariant municipal characteristics (reported in the first six columns of Table 5). In the presence of nonrandom sorting, some of these characteristics would likely differ systematically between treated and untreated municipalities around each threshold. The attributes we examine are the area of the municipality (measured in squared Km) and its geographical location (altitude, latitude, longitude, and distances to the state and federal capitals). We inspect for balance by testing for the presence of discontinuities in these attributes at the pooled thresholds. Reassuringly, we do not

> 10Throughout the empirical analysis, we use municipality-year
services sectors are excluded from the sample, as we focus mostly on manufacturing goods for which we have available international trade data. Thus, we end up with a balanced panel of 701 firms.

As our main measure of IT use, we selected computers-per-worker, a measure which has been used in previous studies such as Bloom et al. (2015). As these authors argue, the main two advantages of this indicator is that it is a physical quantity measure that is recorded consistently across firms and sectors and it avoids the use of price deflators.

Additionally, to evaluate the robustness of our results we also use two additional proxies of IT use. The first one is the share of labor that uses Internet in the firm, and the second one is the share of labor with computer, which should be almost equal to computers-per-worker and is used mainly in our robustness tests. In fact, Bloom et al. (2012b) use the share of labor with computer as a robustness test as there could be measurement in their IT stock capital variable that could be biasing their results.

# **4.2 Trade data**

Trade data were obtained from the World Integrated Trade Statistics (WITS) using the HS 1996 classification at the 6-digits level. Considering that some HS6 codes appear in more than one NAICS 4-digits code, we reclassified the ones that had more than one match using the Mexican Tariff Classification (8 digits), by including them into the NAICS sector that had the highest share of the 6-digits HS code for total Mexican imports in 2013.<sup>7</sup> Competition is measured for the period of 2000-2008. This period was selected considering that the first year of our panel is 2008 (ENTIC 2009 gets information for the previous year). We use imports data for Mexico and the U.S. in order to analyze Chinese competition. Specifically, we calculate the share of China on imports for each NAICS-4-digits code included in the base.

## Where

_Mj, China_ =
set, a large cross-sectional survey of adolescent students in Indonesia that is the baseline of an impact evaluation of a school-based program, and the measures of the outcomes and the main variables of interest. Section 4 describes our methodological approach and the limitations of our data and analysis. Section 5 presents the results for our three research questions. We then conclude with the possible policy implications of our findings.

# **2. What are aspirations and expectations?**

Aspirations are desires for the future that motivate. They can be about any desirable goals: completing a given education level, working in a dream job, getting married, or reaching a high social status (Ray 2006). They depend on awareness about possible opportunities, for example knowing about various educational pathways or types of jobs. They also depend on preferences, which are heavily influenced by the social groups one belongs to (Ray 2006; Genicot and Ray 2020). Once set, aspirations drive choices and effort towards them (Dalton, Ghosal, and Mani 2016). In this paper, we study educational aspirations, conceptualized as preferences for the ideal future completed level in a hypothetical scenario in which one had no constraints.

Expectations are the beliefs of the most likely outcome given one’s perceived current situation and constraints (Bernard and Taffesse 2014; La Ferrara 2019). They may coincide with aspirations, and their formations are undoubtedly related. However, expectations capture a different concept: one’s perceived likeliness to achieve one’s goals. Note that constraints are _perceived_ , not necessarily _actually_ impossible to overcome. For example, students can perceive the actual constraint of the amount of money they and their family can allocate to their schooling. However, they can also perceive economic constraints, such as schooling costs, which may or may not be actually binding, for example if the schooling cost can be overcome with a scholarship.

A handful of studies with measures of both aspirations and expectations suggest that they are different concepts. Given that aspirations are
particularly strong relativism, whereby own income does not matter to subjective well-being independently of income relative to the mean in the area of residence. (The next section points to some reasons to question this finding.) The bulk of the evidence has been for relatively rich countries. The work that has been done for developing countries has been less supportive. Senik (2004), Kingdon and Knight (2007) and Ravallion and Lokshin (2010) tested for relative deprivation effects in self-reported happiness in Russia, South Africa and Malawi respectively and found rather little support for the idea and even evidence of positive external effects of higher ―neighbors’ income,‖ rather than the negative effect predicted by the theory of relative deprivation.

> 21 Similarly, see Pradhan and Ravallion (2000), using data for Jamaica and Nepal, Bishop and Luo (2006), using data for urban China, and Rojas (2007) using data for Mexico. For a more general discussion of economies of scale in consumption in developing countries see Lanjouw and Ravallion (1995).

> 22 See Oswald (1997), Frank (1997), Frey and Stutzer (2002), Clark, Frijters, and Shields (2008) and Clark and Senik (2010). Reviewing the evidence, Frey and Stutzer assert that ―There is little doubt that people compare themselves to other people and do not use absolute judgments‖ (p.412). This would seem to be over-stated.

12
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Karlan, Dean, Ryan Knight, and Christopher Udry (2015) “Consulting and
and public transport services in the community, which are suggested to be inserted into the household questionnaire or community questionnaire as a standalone section. Since the information on transport expenditures and ownership of vehicles can be captured by the separate modules of household non-food expenditures and durable goods in the typical LSMS surveys, they are not included in these prototype modules.

# **5.1. Transport Questions to Be Integrated into the Existing Modules of LSMS Surveys**

This section identifies a number of questions to integrate into existing modules of LSMS surveys based on questions found in different LSMS surveys. It also complements the usual questions with new questions, not reviewed in the rest of the paper, that relate to road safety, pollution, and climate change. First, injuries or deaths from road accidents affect the socio-economic opportunities of households. Introducing a question to know whether such a negative shock has affected the household can be used to quantify the gains for households from improving road safety. Second, transport is major source of CO2emissions and local pollution. More questions to understand how household members choose among modes of transport and make decisions about vehicle ownership is helpful to understand the gains and distributional impacts from policies aiming to reduce transport emissions. Household surveys are a good source of information to understand the trade-off between more mobility and the economic gains in terms of jobs and access to services, and the need to reduce CO2 emissions.

# **5.1.1. Ownership / durables section**

The durable section often includes a question on ownership of vehicles that has been harmonized across countries (see Section on harmonized datasets for an example). We suggest two additional questions on the age of the vehicle and the type of fuel used to better understand two trade-offs informative for the climate change and pollution literature. Buying an older vehicle might be cheaper but older vehicles often pollute more. In many countries, most owned vehicles are second-hand vehicles, often imported from richer countries where newer restrictive regulations are enforced. Question 2 can be used to