rafmacalaba/data-use-mentions-extended huggingface dataset:
_Table 2.2 Years_ _<u>of Schooling and Gross Domestic Product per Capita in Mexico, 1960–90</u>_

|_Year_|_Average schooling_<br>_(years)_|_Ln (GDP per capita_<br>_in 1980 U.S. dollars)_|
|---|---|---|
|1960|2.76|7.95|
|1970|3.68|8.29|
|1980|4.77|8.71|
|1985|5.20|8.63|
|1990|6.72|8.67|

_Source:_ Calculations based on Barro and Lee data set. The World Bank.

With respect to changes in the distribution of schooling by socioeconomic groups, there are several aspects to be considered. In particular, three are examined here: the changes in this distribution that are related to gender, economic sector, and age.

Table 2.3 shows the distribution of schooling by gender from 1988 to 2002. Even though there were clear improvements for both males and females, which signify an upgrade of educational attainment, women achieved a better performance during that period, especially at the top of the distribution. Improvements for males, in contrast, were spread more evenly over the entire distribution. Nevertheless, in 2002 women were undoubtedly more educated than men, as their cumulative distribution dominated that of men (see Figure 2.2).

12
they do not need to trust each other or an intermediary (i.e., “social scalability”). Raskin, Saleh and Yermack (2019) contend that non-state digital currencies could have important welfare implications for emerging markets as they can provide for an alternative asset which could serve as a check on the inflationary tendencies of the sovereign, suggesting that crypto-assets adoption may be driven by a diversification opportunity for local investment. Regarding Decentralized Finance (DeFi), Harvey et al. (2021) claim it has the best potential to provide financial services in the future to overcome the inherent challenges of the traditional financial sector of centralized control, limited access, inefficiency, opacity, and lack of inter-operability.

Early work that aims to identify drivers of crypto-assets adoption includes Hileman (2014) which proposed a Bitcoin Market Potential Index based on seven country characteristics (technology penetration, international remittances, inflation, size of informal economy, financial repression, historical financial crises, and bitcoin penetration) and posited that the greatest potential for adoption lies in Latin America and Sub-Saharan Africa. Additional relevant factors for cryptoassets adoption may also include high expected returns from speculative investment and regulatory arbitrage, particularly related to illicit financial activity (e.g., IMF (2021) and Saiedi, Brostrom & Ruiz (2020)). Feyen, Frost, Natarajan, and Rice (2021) propose a set of supply and demand side drivers of crypto-assets adoption with a focus on stablecoins. As supply side drivers they include profitability and costs of traditional payment service providers, and the availability of infrastructures such ICT and agent networks. On the demand side, they consider cost and inconvenience, confidence in financial incumbents and the government, and macroeconomic conditions.

There is a growing empirical literature that explores several of these drivers. Saiedi, Brostrom & Ruiz (2020) find some evidence that perceived failings of the traditional financial system contribute to the adoption of crypto-assets. However, using U.S. Survey of Consumer Payment Choice data Auer and Tercero-Lucas (2021)
>(0.024)|-0.015<br>(0.020)|
|Observations|2,125|2,075|2,125|2,075|2,125|2,075|2,125|2,075|2,125|2,075|
|R-squared|0.162|0.321|0.158|0.319|0.161|0.322|0.159|0.319|0.159|0.319|
|Number of households|717|717|717|717|717|717|717|717|717|717|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|
|Year FE|YES|NO|YES|NO|YES|NO|YES|NO|YES|NO|
|HH FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Region-round FE|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|

_Note:_ Dependent variable is household log total consumption. Data used are from the three waves of the GHS and telephone survey for conflict. All regressions are conducted using weights. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level. Significance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1 37
A historic achievement by the Land Act was that, for the first time, it recognized married spouses’ right to own land, eliminating earlier prohibitions on married women holding land title (Shale 2019).<sup>6</sup> This is similar in scope to the demise of coverture in the United States in the 1850s, an intervention shown to have affected human capital accumulation (Geddes _et al._ 2012), employment choice, financial sector development and levels of industrialization (Hazan _et al._ 2019).

To improve access to documented land rights and functioning of land and mortgage markets, procedures were streamlined to remove barriers to registering land rights and reduce transaction costs of registration. Regulatory reforms also simplified planning standards, eliminated the need for Ministerial consent prior to lease issuance, and established the LAA as a one-stop shop to speed up registration.<sup>7</sup> Quality of registry data was to be improved by creating a digital register that would link textual to spatial records and facilitate maintenance through interoperability with other registries so banks could be confident in using land as collateral that could actually be repossessed in case of default.

The initial project design aimed to evaluate program impacts based on a randomized roll-out of systematic registration activities. Contamination of the control group made it impossible to adhere to this strategy and resulted in its replacement with a DID strategy in 2013. To this end, four of Maseru Municipality’s Councils (MMCs), spatial units elsewhere referred to as wards in Maseru’s Northeast where systematic regularization activities had not yet started when this defect was discovered, were selected for impact evaluation. Their boundaries and location relative to Maseru city are illustrated in figure 1. Wards MMC 1, 2, and 3, then next in line for implementation, were designated as treatment and ward MMC 27 as control.

This design raises several concerns. First, as the wards chosen for impact evaluation are located at the fringe of Maseru in areas with lower settlement densities, the extent
# and additional policy changes.

# _Unrealized expectations_

The first impact is unrealized expectations. Given the complexity of the sector, public misunderstanding is common. This can lead to unrealistic expectations about the scale and timing of production and its benefits. Over-optimistic projections and statements by companies and International Financial Institutions (IFIs) are also likely to have caused and exacerbated inflated public expectations in many of the countries we analyzed. The role of government is less clear cut. In some cases, the government has attempted to temper those expectations and align them better with reality. In others, government and politicians have only inflated them further.

Earlier studies have documented this impact. Cust and Mensah (2020) find a jump in citizen expectations after oil finds in Africa, as measured by survey responses regarding their personal economic outlook using Afrobarometer public polling data. In Tanzania, a local research institute has been mapping citizen expectations around the gas sector. In 2015, only five years after the gamechanging offshore discovery in 2010 and still some years from FID, 53 percent of citizens surveyed believed production was already taking place.<sup>21</sup> The survey also highlighted over-optimistic expectations of how transformative gas production could be: 40 percent believed that they or a family relation would be employed in the sector, equivalent to more than 4 million jobs if extrapolated (Twaweza, 2015).

Despite Liberia’s very limited oil resources, its potential benefits have captured the imagination of citizens on multiple occasions, as discussed by Collier (2016). Confusion erupted in 2013 when African Petroleum reported a pre-drill estimate for its Bee Eater prospect (African Petroleum, 2013). But the 840 mbbl figure in estimated recoverable resources was mistakenly interpreted in the media, as if Liberia was starting to produce 840 barrels of oil a day (see, for example: All Africa, 2013). Another newspaper ran a headline “If last week’s announcement by African Petroleum that there’s a huge deposit of oil offshore Liberia is anything to go
>**

<mark>The exposure sub-index is comprised of the following six variables measuring exposure to variability and extreme values of temperature and precipitation as well as to natural disasters: (i) Standard deviation of the average monthly temperature 1950-90 (see formulas in Appendix 1); (ii) the range between maximum and minimum average monthly temperature; (iii) the frequency of extremely hot or cold months, defined as the frequency of months in which the average temperature exceeded 30 C or fell below - 10 C; (iv) the frequency of extremely dry months in the spring (less than 5 ml total precipitation per month) and summer (0 ml total precipitation per month); (v) the standard deviation of monthly total precipitation; and (vi) the frequency of weather related disasters between 1998-2009.</mark> <mark>The sensitivity sub-index is comprised of five variables measuring agricultural, demographic, health, poverty, and disaster-related sensitivity to climate change and variability. Sensitivity of agriculture to impacts of climate change and variability is measured as the average of three variables: Area of irrigated land per capita, the degree of diversification of crop land-use measured by the Herfindahl index, and the share of</mark> <mark>7</mark>
are on the Maritime Silk Road. These improvements are subsequently grouped into 75 projects when they are described jointly in our sources (for instance, the Sihanoukville Port project consists of three improvements: a port, a road, and a railway). In Table A1, Belt projects are grouped along six land corridors described by Office of the Leading Group for the Belt and Road Initiative (2017) and in Table A2, the Road projects are grouped along three sea passages described by the Government of China (2017). Table A1 includes a few examples of African rail projects that were attributed to the BRI by alternative sources, though Africa is not included in these corridors. In our analysis of welfare impacts, we will focus on the 68 projects from the Belt and Road that are in Eurasia. Figure 1 shows these projects.

These 68 projects are overlaid on the existing network of all major rail, road and port infrastructure in Asia, Europe and the Middle East (i.e., Eurasia), beginning with existing road and rail connections documented in Natural Earth (Kelso and Patterson, 2018). In cases where Natural Earth does not include connections between cities, we include on the network additional road connections from OpenStreetMap. In China, rail data are further supplemented using Li’s high-speed railway shapefiles (Harvard Dataverse, 2016). Ports included on this network are those handling more than 5 million twenty-foot equivalent units (TEU) in 2015 (Nightingale, 2016). TEU is an inexact unit of cargo capacity used in the shipping industry, corresponding to the volume of a 20foot-long intermodal container. Sea links between ports are derived by assuming ships can take the most direct feasible route between ports. To our best understanding, the resulting transportation network includes all major highways, railroads and ports in Eurasia.

10
|3,647|0.50|3,647|0.55|
|Cash transfer|||3,633|0.17|3,637|0.22|
|Payment deferrals|||3,636|0.09|3,635|0.12|
|New Credit|||3,636|0.07|3,635|0.08|
|Fiscal relief|||3,631|0.09|3,635|0.12|
|Wage subsidies|||3,638|0.40|3,640|0.44|

_Source_ : Enterprise Surveys COVID-19 Follow-up Surveys.

_Notes_ : Includes the 3,647 firms that reported whether they received government support in Rounds 1, 2, and 3. Some firms did not specify the type of support, which is why the number of observations varies.

**<mark>Table 2: Number of Government Support Rounds Received</mark>**

|**No. of Rounds**|**Rounds**|**Obs**|**Mean**|
|---|---|---|---|
|**_Considering all 3 Rounds_**||||
|3|All 3 Rounds|3,647|0.09|
|2|Rounds 1 and 2|3,647|0.07|
|2|Rounds 1 and 3|3,647|0.07|
|2|Rounds 2 and 3|3,647|0.05|
|1|Round 1 only|3,647|0.20|
|1|Round 2 only|3,647|0.03|
|1|Round 3 only|3,647|0.05|
|0|No Rounds|3,647|0.45|
|**_Considering Rounds 1
the cost of international travel (Appendix Table A6). Total expenditures for foreign tourists from the survey data are allocated to each province using the information on the share of time (the number of days) each tourist spent at each destination.<sup>15</sup> For domestic tourists—or Panamanian residents that travel within

> 15 Database on foreign tourists’ expenditures includes expenditures for all destinations and information on which destinations tourists visited on the same trip, but no information on expenditures by destination. The estimation of expenditures by province is done here using two methods. First, total expenditures are disaggregated by province in the same proportion as the share of time (the number of days) all tourists in the sample spend in each province. An adjustment is made for a slightly higher level of average spending by tourists that visit only Panama and Colon. This is the lower bound on the estimate of total expenditures by province reported in Table 4. Thus, this approach takes the total aggregate expenditures by all tourists in the sample and allocates them to each province in the same proportion as the share of time spent in each

11
_American Economic_<br>_Review_|113 countries|Better measuring income growth<br>with night lights data|SCI can help provide more accurate estimates of<br>country income growth.|
|6|Hu & Yao (2022)|_Journal of_<br>_Econometrics_|162 countries|Estimating the relationship<br>between nighttime light growth<br>and GDP growth|SCI can help provide more accurate estimates of<br>country GDP growth.|
|7|Jacob (2017)|_World Development_|145 countries|Impact of data gaps on<br>Millennium Development Goals<br>achievement (MDG)|Stronger country statistical capacity increases the<br>probability of MDG success.|

37
MNREGA induces these changes by tightening local labor markets which raises the opportunity cost of schooling. As a result, boys leave school to provide labor in the market and girls substitute into domestic work.

Overall the existing literature on MNREGA suggests that it had positive impacts on wages, incomes, and potentially health outcomes. It is clear that MNREGA had meaningful impacts on local labor markets. In particular it appears to have tightened labor markets, especially for unskilled labor, by providing an outside option for unskilled labor in the form of public works at a wage that may have been above the prevailing agricultural wage and therefore made unskilled labor more expensive.

# **3 Research design and implementation**

I follow much of the existing literature analyzing MNREGA and utilize a difference-in-differences framework that takes advantage of the phased roll-out of MNREGA across the country to examine its overall impact on the use of fires in agriculture. As a robustness check, I also utilize the treatment pattern from the MNS experiment to determine if improving the implementation of MNREGA leads to an increase in agricultural fires. The design and results of this robustness check are described in Appendix 6.

## **3.1 Data**

**Agricultural fires:** The source of the raw data on fire presence comes from NASA’s Fire Information for Resource Management System (FIRMS).<sup>16</sup> The FIRMS data provides the latitude and longitude and detection time of fires around the world using imagery from the MODIS and VIIRS imaging platforms. For all the primary analysis I use only data from MODIS because VIIRS does not provide sufficient temporal coverage.<sup>17</sup> MODIS Aqua imagery is available from mid-2002 to the present. Imagery from the satellites is collected every 6-12 hours around the world and processed using NASA’s image processing algorithm to identify fires based on the emissions of mid-range infrared radiation. The algorithm is designed to filter out spurious signals

(e.g. solar glare and gas flaring). NASA
> PI scores reflect a quantification of qualitative perceptions on an ordinal scale and are thus subject to noise. Sampling is nonrandom since respondents choose whether to participate, possibly leading to selection bias. This creates difficulties when comparing small changes between countries and within countries over time. An additional issue relates to year-on-year comparisons, which suffer from the limitation that respondents grade performance on a qualitative scale that could be affected by indexing issues, meaning that respondents’ standards of what constitutes “high” or “low” efficiency and quality of logistics services can differ. In <mark>each edition, several countries could not be featured in the LPI as too few assessments from logistics service providers based outside the country in question were obtained. Survey fatigue was a growing issue.</mark> <mark>In recent years, supply chain data has changed from scarce to abundant. Digitalization of supply chains expanded swiftly. Shipments are tracked from origin to destination in many industries and across borders. This has been driven by the rapid digitalization of logistics processes (e.g., Internet of Things) and the growing need for traceability, in parallel with the fast pace of automation in public agencies (e.g., port community system, customs automation). Consequently, global datasets are available, albeit mode-specific.</mark> The 2023 edition introduces quantitative key performance indicators (KPIs), based on shipment tracking datasets, measuring the speed of trade around the world. The KPIs are derived from millions of international movements of containers, aviation shipments, or postal parcels. KPIs measure delays at ports, airports or assess international connectivity (e.g., number of international connections). These

> 5 See <u>https://sdgs.un.org/goals</u> and <u>https://www.weforum.org/publications/the-global-enabling-trade-report2016/</u>

2
Table A.3: International comparison of top average incomes (2019 PPP USD 000’s)

||Top 0.1%|Top 1%|Top 10%|
|---|---|---|---|
|_Pivotal benchmark countries_||||
|China|1,687|358|103|
|France|2,429|637|190|
|India|1,047|273|72|
|South Africa|1,365|457|155|
|United States|7,050|1,581|379|
|_High income share_||||
|Angola|2,614|605|135|
|Bahrain|5,919|1,567|359|
|Central African Rep|314|74|15|
|Mozambique|396|92|19|
|Yemen|324|100|24|
|_LAC (good data quality)_||||
|Argentina|3,697|1,313|427|
|Brazil|1,308|509|143|
|Chile|2,927|946|232|
|Colombia|1,033|447|138|
|Costa Rica|1,247|580|157|
|Dominican Rep|2,086|700|176|
|Ecuador|483|208|70|
|El Salvador|421|207|66|
|_Honduras (WID data)_|514|199|56|
|Mexico|1,862|828|192|
|Peru|1,205|448|115|
|Uruguay|1,282|519|173|

_Note:_ This table presents the average income of the top 0.1%, top 1% and top 10% highest earners in selected countries in 2019 (PPP USD 1,000s). Data is from the World Inequality Database (WID). The figures for Honduras in this
*

||(1)<br>Moderate or severe food<br>insecurity|(2)<br>Food insecure|(3)<br>Raw score|
|---|---|---|---|
|Wartime dummy|-0.014|-0.025|-0.073|
||(0.017)|(0.015)|(0.106)|
|Tigray × Wartime dummy|0.384<sup>***</sup>|0.261<sup>***</sup>|1.607<sup>***</sup>|
||(0.052)|(0.033)|(0.236)|
|Household fixed effects|Yes|Yes|Yes|
|R-squared|0.008|0.004|0.007|
|Mean dep. var (pre-war)|0.361|0.557|2.054|
|No. observations|14523|14523|14523|

Notes: The outcome variable in the first column stands for a binary indicator assuming a value of 1 for those households classified as moderate or severe food insecure based on the severity of food insecurity generated from the eight FIES questions. The dependent variable in the second column stands for binary indicator assuming a value 1 for 25
category are tea, sugar, beer, tobacco, and petroleum products (gasoline, kerosene, and so forth).<sup>35</sup> The CEQ exercise covers only a subset of these as the HIECS provided limited information for mapping them to households’ expenditures (diesel, lubricating oils) or the products could not be identified in the data (wheelchairs and so on).

We include the following products as part of the estimation of excise taxes: tea (with a differential tax for that distributed by ration cards or not), sugar (with a differential tax for that distributed by ration cards or

> 32 Note that by applying _statutory_ rather than _effective_ sales tax rates to purchases recorded in the HIECS 2015, this CEQ assessment does not take a stance on indirect tax evasion or, for example, actual rates of taxation in informal markets.

> 33 This study follows the methodology developed in the _Commitment to Equity Handbook_ (Lustig 2018), chapter 7, to allocate the _indirect impact_ of indirect taxes on the prices of goods and services acquired in the private market. The handbook suggests solving a price‐shifting model—with an input‐output matrix as the empirical description of price determination in the production side of the economy—assuming _inelastic_ demand for all goods and services and fixed technologies of production. That is, producers “push” any input taxes paid (subsidies received) onto the final price of the goods and services, thereby raising prices (lowering prices) relative to a no‐tax (no subsidy) counterfactual.

> 34 Law No 11 of 1991 and subsequent amendments.

> 35 Other items listed in the law are automatic wheel chairs, artificial human organs, vegetable oils, and hydraulic cement.

25
To develop a baseline “no climate change” scenario, we use historical daily climate data for 1997-2006 (i.e., mean, minimum and maximum surface temperatures and precipitation) retrieved from the NASA POWER database (Stackhouse, 2010).<sup>2</sup> A random 50-year baseline climate sequence was generated using bootstrapping techniques based on the historical annual data. In other words, one year of climate is drawn from the historical data fifty times. This technique preserves intra-annual correlations (and higher moments) but not inter-annual correlations. For the purposes of the crop modeling, the distinct wet and dry seasons in Tanzania limit the impact of inter-annual variation in soil moisture at the start of the growing seasons rendering this approach an acceptable compromise in the face of existing data limitations. Our baseline scenario therefore assumes that future weather patterns will retain the characteristics of recent historical climate variability. The principal purpose of the baseline scenario is to provide a counterfactual for the climate change scenarios.

For the purposes of projecting future climate in a given region (Tanzania), it is useful to note that the GCMs are calibrated to reasonably reproduce historical climate on a global basis and predict future climate under alternative levels of global greenhouse gases. The historical predictions of a given GCM for temperature and precipitation for a specific region of the globe, such as Tanzania, will not perfectly match the historical data. In other words, output from a given GCM for historical periods may, for example, consistently under-predict temperature and over-predict precipitation. This is also true with respect to higher order moments and time series properties of the climate series.

In sum, while the GCMs provide information with respect to long run trends in average temperatures and potential trends in average precipitation, it is less clear that the outputs of the GCMs provide useful information with respect to daily variation in climate (Schlosser, 2011). As a result, it is inappropriate to directly take the raw output from GCMs and use these in, for example, crop models for