rafmacalaba/data-use-mentions-extended huggingface dataset:
procedure. To the extent that the conditional mean of the distribution may be far away from the food insecurity line and that marginal effects may effectively differ across the distribution, this can be problematic for our food insecurity estimation method. We test the plausibility of our assumption by investigating the heterogeneity in the RSQ effects along the food consumption distribution and we estimate quantile regression models to uncover differences in RSQ effects across conditional quantiles. We first consider our initial specification without interaction and we estimate the marginal effect of the RSQ on the FCS for quantiles ranging from 0.05 to 0.95. Results presented in Figure 6a reveal an inverted U-shaped pattern with a marginal RSQ effect peaking at above 4 around the 6<sup>_th_</sup> decile and falling to around 2 at the 5<sup>_th_</sup> percentile. We are particularly interested in better understanding how the marginal effect varies within the domain of the distribution that corresponds broadly to the subset of households around the food insecurity line and thus susceptible of coming in and out of food insecurity due to climate variability. Based on historical national food insecurity rates, we evaluate this is roughly the 25<sup>_th_</sup> -45<sup>_th_</sup> percentile – interval.<sup>35</sup> Our estimated marginal effects vary only slightly on this interval between – approximately 3 and 3.5 and is broadly in line with our OLS estimate, which does not invalidate the assumption held in the food insecurity estimation method. We also estimate quantile regressions on logged food consumption which permits a utility interpretation of the results. Interestingly, we find the highest marginal effects (around 9%) at the bottom of the distribution, which then decreases at around 7% between the 3<sup>_rd_</sup> and 6<sup>_th_</sup> deciles before markedly declining to approximately 2.5% at the top of the distribution. This is broadly consistent with the notion
.0|3.0|15.2|3.9|
|Average UT, passenger, U.S. cents/passenger-km|0.6|—|1.0|1.6|1.1|2.3|0.8|

_Source:_ Bullock 2009. Derived from AICD railways database, http://www.infrastructureafrica.org/aicd/tools/data. Note: — = data not available; n.a. not applicable.

## **Challenges**

Notwithstanding some improvements made under the concession, the performance of the CEAR still does not compare favorably with networks in neighboring countries (table 3). Relatively high freight tariffs and slow transit times continue to limit the competitiveness of rail vis-à-vis road transport. Moreover, the line is very lightly used—even relative to others in southern Africa—further challenging financial sustainability.

Malawi’s rail links with the rest of the continent are not functioning very effectively. There is interest in improving the performance of the Nacala corridor, which is Malawi’s shortest route to the sea through northern Mozambique, and which is currently beset with performance problems. Another proposal exists to extend the rail network northwards to connect with the Tanzania and Zambia Railway Authority (TAZARA) network; however, the associated costs are high ($200 million) and the economic case is not clear.

# Air transport

## **Challenges**

In common with other small countries in Africa, Malawi has a relatively small air transport market. The domestic market amounts to no more than 167,000 seats, while the international market comes to 435,000 seats. The intercontinental market is negligible. Nevertheless, relative to the population of the country the number of seats per capita—at just over 23—is an order of magnitude higher than would be found in larger countries.

9
government policy and public finances. Consistent with this, about three-quarters of respondents expressed concern about the fiscal situation in their country, and most people who answered the question “Have you ever read the report of the fiscal rules monitoring body in your country?” (90 respondents skipped the question) said “yes.” Of those who said they had not read the report, only 21 percent said the reason was a lack of interest.

**Figure 2. Respondents to Survey on Fiscal Rules in the Western Balkans**

<!-- Start of picture text --> Affiliation Country of residence<br>Media, 21 Albania, 19<br>Other, 40 Bosnia and<br>Herzegovina,<br>46<br>Kosovo, 26<br>Academia, 28<br>Private sector, Montenegro,<br>40 31<br>Serbia, 40<br>Public sector,<br>68<br>Macedonia,<br>FYR, 35<br><!-- End of picture text -->

# **Most respondents said they understood the fiscal rules in place in their country, though the**

**answers also suggest some misunderstandings** . Many respondents knew what a fiscal rule was and were able to identify some or all of the fiscal rules in place in their countries. In Albania, the debt-reduction rule was sufficiently well-known to be identified by two-thirds of the respondents, and in Kosovo a small majority were aware of both the debt rule and the deficit rule. But several respondents did not distinguish fiscal rules from annual budget limits or medium-term fiscal targets (Figure 3). Consistent with this, most North Macedonian respondents thought their country did have a fiscal rule, whereas by the definition used in this paper it does not. And in many countries, the fiscal rules did not seem to be very well understood even among the presumably relatively well-informed respondents. In Serbia, most respondents (incorrectly) thought
case of transfers (Kakwani, 1977). Thus, the Kakwani index will be positive (negative) if a tax is globally progressive (regressive), where global progressivity (regressivity) is in terms of the concentration curve associated with the particular tax lying everywhere below (above) the prefiscal income Lorenz curve. Similarly, in the case of transfers, a transfer is globally progressive ( _in relative terms)_ if its concentration curve lies everywhere between the prefiscal income Lorenz curve and the 45-degree line. If the concentration curve of a transfer lies everywhere above the 45degree line, the transfer is globally progressive _in absolute terms_ .

_Source: Lustig (2018)_

# **3.2. Data and consistency between administrative and survey information**

The analysis is based on the Household Budget Survey (HBS) for 2017 -- the latest year of microdata available at the time of analysis. This is the official survey conducted by the National Bureau of Statistics of the Republic of Moldova for purposes of poverty and inequality monitoring, among other desiderata. The HBS survey sample covers the whole of Moldova’s territory, with the exception of the Transdniestria region. The statistical population includes households / individuals who are citizens of Moldova, including persons absent for long periods of time (exceeding 1 year) if they maintain family ties to the households from which they originate. The HBS sample is drawn from the master sample based on the 2004 Population Census, comprised of 129 localities grouped in 150 primary sampling units. The sample is stratified based on geographic criterion (North, Center, South regions), town size and type of area (urban/rural). In 2017, the survey sample covers 5,149 households or 11,772 individuals and is representative at the national and regional levels.

One of the implications of the HBS sample being based on the 2004 Population Census, and not on the 2014 Population Census (the new sampling frame, taken from the 2014 Population Census, will start being used from the
Brazil, which finds no effects of oil discoveries on migratory flows (Caselli and Michaels 2013).

# **7. Conclusion**

This paper examined the local impacts of Ghana’s offshore oil discovery in 2007. We find that the oil discovery increased real income and employment for households and individuals in districts associated with oil discovery compared to those without such discovery. Income increased by 4 percent for households living in the oil region compared to households in non-oil regions. The increase in income is stronger for workers with higher levels of education relative to less educated workers. We find similar effects when studying employment outcomes. Local employment in general increased by 4.5 percent, with the discovery having an indirect impact on other non-oil local sectors such as manufacturing and construction. There were no significant impacts on agriculture and service employment.

Though we find the effect of offshore oil production on income to be significant, we do not find a statistically significant impact on consumption. This may be because poor households that are mainly involved in the agriculture and service sectors did not benefit from the increase in employment or because of shifts in consumption from nondurable to durable goods.

We note that these results should be viewed as short-run effects because offshore oil discovery in Ghana is relatively recent, and thus we examine the impacts only a few years after the discovery.<sup>25</sup> In order to explore the longer-term effects of oil on local communities, an extension of the time period with a more recent wave of household survey data (GLSS 7) will be carried out. Later waves of household survey will provide in-depth insights on the long-term impacts of oil on local communities.

Overall, the findings suggest that the offshore oil discovery and production can create opportunities for local firms and workers in other sectors. They also indicate that

> 25 The findings are consistent with  Arezki, Ramey, and Sheng (2017) who argue that the news of large-scale oil resource discovery affects macroeconomic outcomes after 5 years due to the lag between
|13|Manufacture of furniture|94|19|16.8|1.0|67|7|9.5|0.3|
|---|---|---|---|---|---|---|---|---|---|
|14|Insurance, reinsurance and<br>pension funding|21|6|22.2|0.3|23|7|23.3|0.3|
|15|<br>Fishing and aquaculture|91|3|3.2|0.2|101|6|5.6|0.3|
|16|Telecommunications|26|10|27.8|0.5|12|6|33.3|0.3|
|17|Manufacture of beverages|7|0|0.0|0.0|10|1|9.1|0.0|
|18|Manufacture of basic<br>pharmaceutical products & preps|27|15|36.6|0.8|||||
|19|Water collection, treatment and<br>supply|9|2|18.2|0.1|||||
|20|Employment activities|52|9|14.8|0.5|||||
||Total employees (in thousand)|3663|1928|34.5|100|3766|2021|34.9|100|

Source: ILOSTAT database, International Labor Organization Services sectors consists of around 40 percent of women in the work force, however, within the services sector male workers dominate all the sectors except education, health and manufacturing. Women predominate in the expected sectors of health and education, as most teachers, nursing and medical staff are female, and are well represented in industry groups that
the characteristics of small geographical units. In addition, nighttime lights have a tendency to extend into neighboring regions, a phenomenon called blooming effect (Small et al., 2005; Abrahams et al., 2018), which complicates the identification of the actual source of lights. Lastly, nighttime light data is saturated at a certain threshold of light intensity which is often exceeded in very bright urban cores, thus making the analysis of nighttime light data at small geographies difficult. The degree to which these shortcomings of the data are relevant for studies in developing counties is however largely unknown, as economic data at small geographies is hard to find in the relevant context.

This paper presents an alternative to nightlights and use Landsat imagery to predict economic activity. It is in the tradition of other papers that have explored the potential of other remotely sensed data products that are not based on nightlights. A variety of studies use high-resolution imagery and convolutional neural network models to extract object features that are then used to predict economic outcomes such as poverty (Jean et al., 2016; Engstrom et al., 2016; Babenko et al., 2017; Perez et al., 2017). The drawback to these approaches is that they tend to use computationally intensive algorithms and rely on expensive proprietary satellite imagery. While these studies have made remarkable advances in the use of remote sensing in economics, applied researchers interested in measuring economic variables remotely might lack the skills or resources to use such methods or data.

This study therefore follows a different route and rely on medium-resolution imagery and simple regression models to predict economic outcomes from sky. It looks at imagery from the Landsat program, a public domain satellite product that has been used to survey Earth over the last thirty years, and investigate its prediction power for economic activity. In contrast to nightlights, Landsat satellites measure light reflectance during daytime at different wavelengths of the spectral band. Having a much finer spatial (30m) and temporal resolution (16 days) than nightlights, Landsat imagery have
. While this literature initially focused on impacts in high-income countries, subsequent estimates have also been made for LMICs where the prevalence of overweight and obesity has been rising quickly (Schneider et al. 2020). One recent estimate for 161 countries (comprising high-income countries, as well as LMICs) is that the global economic losses attributable to overweight and obesity amount to 2.2% of global GDP (Okunogbe et al. 2022). The corresponding human cost was estimated at 4.7 million deaths in 2017 (Dai et al. 2020); an update using 2019 data associated this cost with 5 million deaths due to noncommunicable diseases (NCDs), 77% of these occurring in LMICs (Abbafati et al. 2020). Similarly, the World Bank (2020) estimated that 4 million deaths globally were attributable to obesity and projected that by 2035 the costs of obesity in LMICs will amount to $7 trillion.

There is also literature linking dietary factors directly to NCDs (e.g., GBD 2017 Diet Collaborators 2019), with some dietary factors protecting against and others increasing risks of NCDs. GBD 2017 Diet Collaborators (2019) use a bottom-up methodology for each of 15 dietary factors, adding up projected effects and estimating that these dietary risks accounted for 11 million deaths globally in 2017. The effects of some of the dietary risks (e.g., excess consumption of sugar and fat) may be mediated through obesity; while others may operate independently (e.g., GBD 2017 Diet Collaborators, 2019, estimate that 3 million deaths were attributable to excess sodium intake in 2017). Arguably using a more holistic measure of diet quality is preferable to adding up the risks due to multiple individual dietary factors, and less prone to double counting.

Another literature, even broader in scope, ties malnutrition with climate considerations (e.g., The Food and Land Use Coalition – FOLU – 2019). FOLU (2019) presents estimates of the economic costs of, both what
conservatism,<sup>9</sup> use of social media, exposure to the local feminist movement,<sup>10</sup> as well as a subsample of questions from the Crowne-Marlowe social desirability scale (for robustness checks as in Dhar et al., 2022; Mehmood et al., 2022). The Online Appendix contains a full list of these items.

# **3.2.2 Treatments**

The experiment had two treatment arms: T1 only provides information on gifting. T2 first corrects respondents’ beliefs about public reform support and then provides information on gifting. After the baseline survey, all respondents were asked about their beliefs regarding public support for inheritance law reform:

**[Second-Order Beliefs - all respondents:]** Now let’s talk about the opinions of _other_ Tunisians:

1. Out of 10 _women_ chosen at random from Tunisia, how many do you think believe that daughters should inherit as much as sons?

2. Similarly, out of 10 _men_ [...]?

3. In your opinion, how many of these _(answer)_ women would support a legal reform making gender equality in inheritance the legal norm in Tunisia?

4. Similarly, how many of these _(answer)_ men [...]?

After these questions, respondents were randomly allocated to T1, T2 and C in roughly equal shares. The interviews of respondents who were allocated to C directly continued with the outcome measures. Only individuals in T2 were informed about the true level of support for legal reform:

> 9Respondents were asked whether the sale of pork and alcohol should be legal in Tunisia, whether banks should be allowed to charge interest, whether religious leaders such as sheiks and imams should have a say in Tunisian politics and whether Islamic inheritance law forms part of Tunisian national identity. I construct a measure of religious conservatism based on these items using principal component analysis. The individual items as well as the index measure are
bustness check (see Section 4), we repeat these entry regressions using a version of the GDP-by-field and relative employment variables that is exclusively based on “movers,” namely students who work in a department but obtained their degree in a different one. Table A.4 shows these results, which are practically identical to the ones from panels A and B in Table 3.

We would expect changes in labor demand not only to motivate the opening of new SCPs but also the enrollment of new, additional students—either in existing or new programs. We examine aggregate enrollment effects (Table A.5) and find that, indeed, enrollment in a field and department rises in response to a local demand increase for graduates in the field, as in Grosz (2022). Consistent with entry responses, the enrollment elasticity with respect to labor demand changes is higher for private than public HEIs (1.2 v. 0.9, respectively). Enrollment elasticities are larger than entry elasticities because they account for additional students not only at new but also at existing programs. Nevertheless, the main message from these results is that not only do HEIs respond to economic activity changes; students respond as well.

We further explore heterogeneous responsiveness to local labor market demand by looking at the three HEI types that provide SCPs (Table A.6.) Both the estimates using GDP-by-field and field relative employment show that technological and technical institutes (which specialize in SCPs) as well as technological schools (offering SCPs as well as bachelor’s programs) are more responsive than universities (which teach the full spectrum of degrees, ranging from SCPs to PhDs). Through greater specialization in SCPs, non-university HEIs have an institutional setup readily geared towards SCPs, which makes them nimble and fast. Anecdotal evidence indicates that these institutions are in closer contact with the private sector than universities and operate more flexibly. Because of their larger variety of products, universities lack this flexibility. Based on conversations with university leaders, universities that offer
(Year fixed effects) and a set of dummy variables for the firm’s industry (2-digit ISIC Rev. 4). Thus, annual global shocks to certification are accounted for, as are industry specific requirements for certification. Based on the literature on the determinants of certification decisions reviewed in Section 2.3.3, we include several other controls. At the country-level, we control for overall economic development that is typically associated with greater use of certification and lower informality. The control is the log of GDP per capita (PPP adjusted and at constant 2007 Int’l $). We also control for country-size, proxied by the log of total population in the country-year to capture possible economies of scale in certification. The data source for both country-level controls is WDI (World Bank). In the robustness section, we include several other country-level controls.

Firm-level controls (taken from the WBES) included in the baseline mode are as follows (see Table A2 in the Appendix for a detailed description): labor productivity defined as the log of a firm’s annual sales (in USD and deflated to 2009 prices) divided by the number of workers employed at the firm; firm-size proxied by the log of the total number of workers employed at the firm; log of the age of the firm; log of the number of years of experience the top manager of the firm has in the industry; competition between formal firms proxied by the Herfindahl index of sales concentration defined at the country-year-industry level (Herfindahl index), with higher values of the index implying less competition between the formal firms; annual sales made directly abroad as a proportion of total annual sales of the firm (Exports); the percentage of the firm that is owned by foreign entities (Foreign ownership); a dummy variable equal to 1 if the firm has a woman top manager and 0 otherwise; annual growth rate of the firm proxied by the percentage increase in the number of workers over the last 3 years (Annual employment growth
analysis first proceed by using spectral indices derived from these bands. These measure certain land use patterns and can be easily interpreted. The regressions use the normalized difference built-up index (NDBI) and the normalized difference vegetation index (NDVI) which are non-linear combinations of two Landsat bands each.<sup>9</sup> where _Red_ corresponds to the Landsat band 3 (red light), NIR is the Near Infrared measurement of band 4, and SWIR is the value of the Shortwave Infrared band 5. These indices are designed to measure urban areas and vegetation by capturing typical spectral signatures of these features. Their values range between -1 and 1, and a higher index value corresponds to more vegetation and built-up area presence respectively. For example, the NDVI measure is designed to capture live green vegetation on the ground. For photosynthesis, live plants absorb visible light (low wavelengths) but reflect infrared light (higher wavelengths), thus a higher value of _NIR − Red_ indicates presence of vegetation. The NDVI has been used in the environmental sciences to distinguish vegetation from other land uses. Similarly, NDBI is designed to detect land cover typical of urban areas (Zha et al., 2003).

These indices are chosen for several reasons: 1) They are simple transformations of the raw Landsat data that can be easily calculated by applied researchers. 2) They directly correspond to land uses that are man-made (cities, agriculture) and are plausibly correlated with economic activity. 3) They have been commonly used in other fields and can in principle be replicated with other satellite products that measure spectral light such as Sentinel-2. All measures follow a distribution which can be approximated by a normal distribution, although there are thick tails in the right (NDBI) and the left (nightlights, NDVI) end of the distributions (see density figures in the appendix).

> 9A third commonly used indicator is the normalized difference water index (NDWI) measuring water bodies. In this setting, NDVI and NDWI are highly
Figure 10: Projected diarrhea incidence in children under two climate scenarios for three different levels of toilet ownership (2014, 2021, and 100%) <!-- Start of picture text --> 21<br>20<br>19<br>18<br>17<br>16<br>2015 2020 2025 2030 2035 2040<br>Year<br>SSP1.19 - 2014 toilet adoption SSP1.19 - 2021 toilet adoption SSP1.19 - 100% toilet adoption<br>(a) SSP1.19<br>21<br>20<br>19<br>18<br>17<br>16<br>15<br>2015 2020 2025 2030 2035 2040<br>Year<br>SSP3.70 - 2014 toilet adoption SSP3.70 - 2021 toilet adoption SSP3.70 - 100% toilet adoption<br>Projected diarrhea incidence (%)<br>Projected diarrhea incidence (%)<br><!-- End of picture text --> (b) SSP3.70 Note: Lines of the same color represent the same climate ensemble. They allow us to communicate climate uncertainty stemming from the limitations of climate models. The plain line is the ensemble median, and the dashed ones represent the 10-90% confidence interval. Black refers to the first scenario (2014 toilet ownership), blue to the second (2021 toilet ownership), and green to the third (100% ownership by 2025).

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