rafmacalaba/data-use-mentions-extended huggingface dataset:
and relat<br>350.0<br>Crude materials,<br>300.0<br>inedible, except f<br>250.0<br>Beverages and tobacco<br>200.0<br>150.0 Food and live animals Manufactured Goods,<br>(including chemicals,<br>100.0 machinery and transport<br>equipment):<br>50.0<br>94.6%<br>0.0<br>Source: WITS<br>Source: WITS<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> Source: World Development Indicators (except (c) and (d)).

16
06|
|Wood and Paper Products|10.53|8.24|9.32<br>|22.77|19.83|21.25|
|Chemical<br>Products|11.92|9.92|10.87|11.14|10.0|9<br>10.60|
|Rubber and Plastic<br>Products|13.17|11.64|12.38|32.68|29.28|30.94|
|Stone, Clay and Glass Product|s7.85<br>|4.91|6.21|11.35|12.38|11.85|
|Metal products|8.95|7.27|8.07|14.09|11.25|12.59|
|Electrical<br>Machinery|7.72|7.64|7.68|8.92|11.39|10.07|
|Machinery and Transport|||||||
|<br>Equipment|6.49|<br>6.32|6.40|13.74|13.55|13.64|
|Other|8.79|6.26|7.42|14.92|10.94|12.77|
|Total|8.79|6.26|7.42|14.92|10.94|12.77|

# Source and note:

PPPs from table 3.13. The PPP for food products is the weighted average for grain mill and sugar and sugar products. The PPP for chemical products is a weighted average for soap and detergents, paints, agricultural fertilizers, petroleum refining and products. The PPP for stone, clay and glass products is a weighted average for cement and bricks. In all cases value added figures (US census concept) were used as weights. The
percentile ratios, namely the 90/10 ratio, the 80/20 ratio, and the 90/40 ratio (i.e., the Palma ratio). All income measures are converted to real terms using 2011 PPP dollars. Panel B of Figure 1 provides a global map of income inequality at the subnational level, which shows substantial variation of inequality across regions within a country.

We match our poverty and inequality data with the ERA5 satellite reanalysis data from the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ECMWF). The ERA5 provides hourly estimates of several climate-related variables at a grid of approximately 0.25 longitude by 0.25 latitude degree resolution with data available since 1979 (Dell _et al._ , 2014). An advantage of the ERA5 data is that it combines information from ground stations, satellites, weather balloons, and other inputs with a climate model, and therefore is less prone to station weather bias.<sup>5</sup> For robustness tests, we use the global gridded data from Climate Research Unit of the University of East Anglia (CRU) available at 0.5<sup>◦</sup> resolution. We provide a more detailed description of the data sources, including the list of the countries in each dataset and the summary statistics of the main variables in Appendix B.

# **3. Empirical Specifications**

Different identification strategies were employed to estimate the effects of climate change on economic outcomes (Burke and Emerick, 2016; Dell _et al._ , 2014; Kolstad and Moore, 2020). Early studies mostly used a cross-sectional approach utilizing spatial variation at a point of time, comparing outcomes between hot and cold areas (Mendelsohn _et al._ , 1994; Schlenker _et al._ , 2005). Yet, a key assumption when estimating the coefficients of the climate-related variable from cross-sectional models is that climate change is not correlated with other unobservable factors. Violation of this assumption could result in an omitted variables problem
but are still in need of addressing. Understanding how the crisis might relate to these issues, and what policy opportunities might present themselves in the times ahead is an ongoing challenge.

The focus of this study is on the issue of global financial secrecy and its institutional implications for emerging economies. Many policy discussions on making the global financial system ‘work for all’ focus on financial secrecy: its beneficiaries and losers. Over the last two decades and especially actively since the 2008 financial crisis, policy makers introduced new rules and regulations to counter the negative spillovers from secrecy jurisdictions around the world. However, as with many past crises, the 2020 pandemic is likely to intensify the adverse effects of these jurisdictions for developing countries as capital would try to flee the most vulnerable economies. Developed economies also need more caution and oversight as emergency fiscal spending related to Covid-19 creates a moral hazard and an increased potential for fraudulent activities. Global Financial Integrity (GFI), a DC-based think tank, has already issued warnings about a potential uptick in illicit financial flows even as legitimate economic activity slows down.<sup>5</sup> Scholarly attention to these issues has been growing along with the enhanced policy attention. Besides intensifying public concerns, the infamous data leaks connected to the Panama and Paradise Papers also turned into a productive source of ‘big data’ for examining related issues. This paper reviews the key findings in the literature that explores the drivers and effects of financial secrecy on the emerging economies. We show that research on the drivers of financial secrecy has been focused on issues of tax avoidance, neglecting institutional arbitrage problems that go beyond taxation issues. Additionally, while the literature on economic effects of financial secrecy has advanced from country-level findings to firm-level observations, further research is needed to understand the political and institutional effects of global financial secrecy at the domestic level (in home countries). In the concluding section of this paper, we suggest some promising avenues for future research as well as new items to be included on the
The remainder of the paper proceeds as follows. Section 2 describes our data. Section 3 presents our descriptive graphical findings, while Section 4 presents our empirical results. Section 5 concludes.

# 2. Data and Methodology

# 2.1 Data

We consider a variety of real-time indicators, including data on nitrogen dioxide (NO2), nighttime lights, Google mobility indicators, Google search data, and food price data. NO2 is a pollutant produced by everyday human activity, such as burning fossil fuels. As such, it is attractive as a potential measure of the changes brought about by the COVID-19 crisis, as changes in patterns of human behavior can directly affect NO2 emissions. We use the TROPONOMI Level 2 Nitrogen Dioxide product obtained initially by the Sentinel-5 Precursor, an Earth observation satellite launched as a part of the Copernicus Programme in October 2017. We obtained data from January 2019 to December 2020, with the resulting series containing monthly concentrations of NO2 observed at the subnational level.

We also collected information on monthly nighttime lights intensity from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard the Soumi-NPP Earth observation satellite processed by Elvidge et al. (2017). Nightlights have been found to correlate well with some measures of economic growth and development (see, e.g., Henderson et al., 2012) and may therefore provide a window into possible changes in economic activity brought about by the COVID-19 crisis. We use the Annual VNL Version 1 product that corrects data influenced by stray lights, excludes observations impacted by cloud cover, and has greater spatial coverage (especially in the northern part of the globe; for additional information, see Mills et al., 2013). As in the case of the NO2 series, the resulting data set contains monthly time series on nighttime light intensity observed at the subnational level from January 2019 to December 2020. To account for seasonal fluctuations in NO2 and nighttime lights series, we construct year-over-year deviations in 2020 relative to the same month in 2019, expressed in percentage changes
# **1. Introduction**

This paper draws lessons from a randomized experiment in rural Malawi, which has one of the highest population densities for a Sub-Saharan African country<sup>2</sup> . In order to understand why rural roads – passable for vehicles in rural areas – may not necessarily lead to the provision of regular bus services, a minibus service was subsidized over a six-month period over a distance of 20 kilometers to serve five villages<sup>3</sup> .

Like Hine and Rutter (2000) mentioned, “in the quest to tackle rural poverty, feeder road investment is a favoured solution of many donors.” Feeder roads are proposed as a potential way to take people out for poverty in rural areas (AICD 2009). With this end in view, a benchmark called the rural access index (RAI), which is the proportion of rural people who live within two kilometers (typically equivalent to a 20-minute walk) of an all-season road, has been set.<sup>4</sup> Measuring the cost of being isolated is a growing subject of research in economics and development. Ravallion and Jalan (1997) confirmed the existence of spatial poverty traps and the need for households to reach some asset thresholds before they will participate in markets. Therefore, rural roads investments seem to be critical. Indeed, rural road development enhances access to markets for both inputs and output through a reduction in transaction and trade costs (transport and logistics costs). The greater availability (both monetary and physically speaking) of inputs increases their use by farmers. Consequently, agricultural productivity can increase. Rural roads also allow producers to achieve additional productive opportunities, leading to a rise in production that is highlighted by numerous studies. Stifel and Minten (2008) find, in the case

> 2 According to official population estimates in 2002, population density was more than 90 inhabitants per sq.km.

> 3 This location was selected in order to make multiple trips per
**Figure 6: Change in Relative Employment by age group. Restricted to workers with**

<!-- Start of picture text --> college.<br><!-- End of picture text --> <!-- Start of picture text --> A. Age 23-44 B. Age 45-65<br>2002 2004 2006 2008 2010 2012 2002 2004 2006 2008 2010 2012<br>Year Year<br>Professionals Education Directors Supervisors Professionals Education Directors Supervisors<br>Admve Support Retail Rest Admve Support Retail Rest<br>Notes: Authors’ calculations using quarterly Labor Force Surveys from 2000 to 2012. Relative employment refers<br>to the share in employment of that occupation out of total employment. Restricted to workers with college<br>education. Lines are smoothed by a moving average with a 0.4 weight to the current observation and 0.3 to the<br>previous and post observation.<br>Figure 7: Monthly Earnings by age group. Restricted to workers with college.<br>A. Age 23-44 B. Age 45-65<br>2002 2004 2006 2008 2010 2012 2002 2004 2006 2008 2010 2012<br>Year Year<br>Professionals Education Directors Supervisors Professionals Education Directors Supervisors<br>Admve Support Retail Rest Admve Support Retail Rest<br>30 30<br>25 25<br>20 20<br>15 15<br>% Relative Employment % Relative Employment10<br>10<br>5<br>5<br>25000 30000<br>20000 25000<br>15000 20000<br>Monthly Labor Earnings10000 Monthly Labor Earnings15000<br>5000 10000<br><!-- End of picture text --> Notes: Authors’ calculations using quarterly Labor Force Surveys from 2000 to 2012. Relative employment refers to the share in employment of that occupation out of total employment. Restricted to workers with college education. Lines are smoothed by a moving
*|0.137***|0.109***|
||(0.174)|(0.079)|(0.164)|(0.088)|(0.134)|(0.086)|(0.043)|(0.024)|
|p-value|0.000|0.000|0.000|0.000|0.000|0.000|0.002|0.000|
|Test T1 >= T2 (p-value)|0.216|0.087|0.216|0.177|0.206|0.080|0.242|0.185|
|Endline control mean|||2.2|22|||0.|152|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|
|(Pseudo)R-squared|0.071|0.531|0.016|0.122|0.025|0.223|0.023|0.271|
|Observations|2,382|2,382|2,382|2,382|2,382|2,382|2,160|2,160|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Poisson and probit models (Columns (3), (4), (7) and (8)) show average marginal effects (AME). Number of ISFM technologies adopted ranges from 0 to 5. Integrated adoption of full ISFM package is a dummy variable. Additional baseline control variables at household level are age, gender and education (in completed years) of HH head; whether HH participated in off-farm work or a nonfarm business activity; number of HH members above age 14; walking distances to nearest farmer training center, paved road and market (in minutes);
such that farm households only consider the ratio of the marginal value product of their labor in agriculture and non-farm rural wages in deciding where to work. This contrasts with the baseline scenario in which farm households include the returns to land in their decision to work on- or off-farm, since leaving the farm means forgoing farm land (recall equation 5).

Finally, we add the further opening of China’s economy to world trade through WTO accession. The WTO accession scenario is described in detail in Table 4. Import tariffs are reduced gradually and the sectoral reduction rates are aggregated from Harmonized Commodity Description and Coding System (HS) tariff schedules for the period of 2002-2007 and weighted by 1997 ordinary trade data. The quota control system is introduced for rice, wheat, corn, cotton, wool, vegetable oil and sugar and the growth rate of their quotas are accelerated during the simulation period. Textile and clothing quotas on exports to North America and European markets are phased out completely by 2007. Following Francois and Spinanger (2004), we model the impact of service sector liberalization as halving the barriers to services trade. We also introduce a 20% productivity boost for the automobile sector to reflect the efficiency gain from industrial restructuring and realization of economic scale in this sector after China WTO accession (Francois and Spinanger, 2004). As noted above, we implement this accession scenario in two different ways: first, in conjunction with labor market reforms (WTO-L) and secondly, in their absence (WTO). By comparing these two outcomes we are able to assess the potential interaction between labor and product market reforms in China.

# **5. Simulation Results**

# **Labor Market Reforms**

**_Aggregate results:_** The aggregate results from these simulations are reported in Table 5.

18
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jobs would predict that rates of formality increase with age as individuals repeatedly attempt to access good jobs and never leave them. In such a model, the age-formality gradient reflects the arrival rate of formal jobs, i.e. the degree of rationing. In contrast, other theories of informal that or non- employment emphasize comparative advantage pecuniary benefits do not structurally produce life cycle dynamics (Clark et al. (2017)).

We find that cohorts of Egyptian men who exit school when labor market conditions are bad do not exhibit the same persistent scarring effects identified in rich countries (e.g. Kahn (2010b), Oreopoulos et al. (2012)). Instead, they are worse off for at most a few years and only in terms of the quantity but not the quality of employment. More puzzling, good initial conditions in fact are associated with negative outcomes after 5 or 6 years of potential experience. For example, those who faced a better labor market when they exited school (as measured by high employment rates) are less likely to be employed, to have social security coverage, to have a full time job, to have a contract, and to be wage workers, and they work fewer hours, especially towards the end of their first decade of labor market experience.

We that these results that variation in labor market con- hypothesize reflect cyclical ditions dominates effects. Cohorts that enter into a bad labor market end scarring up profiting from eventual economic recoveries, and are not strongly penalized by having a bad start. The negative correlation between initial employment rates and eventual employment quality could also be consistent with a model of queuing in which some cohorts forgo initial employment opportunities in order to secure rationed quality jobs down the road (and vice versa). We concluded that data sets spanning longer periods are needed to confirm either hypothesis.

# **2 Data**

## **2.1 Measures of informality**

Three measures of informal employment are most often used in the
Landscan has two key advantages and two key disadvantages over other data sets. First, Oak Ridge National Lab is more explicit in the fact that they are trying to estimate the ambient population with potentially nocturnal and diurnal populations; while in other algorithms this is implicit through the general smearing of the population into built cover, without workplace or residence distinction. The second advantage of Landscan is that Oak Ridge National Lab has access to information which would improve precision over the use of Landsat to just assign people to built cover. Oak Ridge has access to very high-resolution satellite data (under 10-40cm) which _potentially_ allows them to distinguish building types based on what building shapes are likely to house employment versus residents (versus commercial activities like shopping), as well as _potentially_ to distinguish roads for commuting and even infer building heights with digital elevation modeling [DEM]. A key disadvantage in using Landscan is the complete lack of specificity and transparency as to what Oak Ridge researchers actually do; hence, our use of the word ”potentially” in describing what they might do. The second disadvantage, which they acknowledge, is that Landscan data for different time periods are not comparable over time, presumably both because of differential availability of high-resolution data over time and increasingly sophisticated extraction of information from later satellite images.

Hopefully in the future, proposed data sets such as the High Resolution Human Settlement Layer (HRSL)<sup>3</sup> or Modelling and Forecasting African Urban Population Patterns (MAUPP)<sup>4</sup> – which also use very high spatial resolution data – will be able to cover a wider set of time periods and countries consistently with a more explicit methodology. Then one will be able to compare them with Landscan and do more comprehensive ground-truthing exercises. Second while ambient population may be a relevant measure to use in characterizing economic density, one might prefer to know about clustering and density of employment. If the assignment of people to workplace buildings gets very sophisticated, we would be able to explore
Africa<br>(2010)<br>Gini coefficient<br>Gini pooints<br><!-- End of picture text --> _Source: Authors’ calculation based on UNHS 2016/17; CEQ Institute database; and World Bank (2018a). Note for b): The year for which the analysis was conducted is indicated in parenthesis. The graph is ranked from the smallest to the largest redistributive effect, which is the change in the Gini coefficient from market income plus pensions to final income. Except for Kenya, where the effect is the change from market income to final income. When the redistributive effect is positive, inequality declines, when it is negative, it increases._ 21
Kerala - the focus of this paper - is regarded as a model of social development in the developing world. When last recorded in 2014, life expectancy was 74.9 years, the highest among states in India. The literacy rate was 94 percent in 2011, compared with 73 percent for India as a whole.<sup>9</sup> Yet despite these laudable achievements in health and education, Kerala has long fared poorly in terms of job creation, suffering from double-digit unemployment rates for decades.

Since the 1960s, these poor prospects at home have compelled Keralites to pursue jobs both in other states within India and abroad. In the 1970s in particular, rising oil prices in the Arabian Gulf countries and the resulting construction boom fueled a significant demand for temporary labor, mostly in low-skilled manual work. Keralites responded to this demand in sizeable numbers (Zachariah et al., 2003). By 1998, 7.3 percent of Keralite households had at least one member in a foreign labor force. In 2003 (the end of the five-year period examined in this study), that figure had risen to 8.4 percent. More recent data from the 2014 wave of the KMS survey show that international migration continues to trend upward, with 18 percent of households having at least one member in a foreign labor force that year.

The share of Keralite households with one or more members participating in the labor force of other Indian states has been considerably lower over time. Between 1998 and 2003, it increased marginally from 3.2 percent to 3.4 percent. By 2014, 4.9 percent of Keralite households had members working in another Indian state (KMS, 2014).

Domestic (or interstate) and international migration from Kerala appear to be competing options for households, with one almost always being chosen over the other. Only infrequently does a single household send family members both overseas and to another Indian state. In 1998, approximately 6.5 percent of migrant households had both domestic and
<!-- Start of picture text --> Figure 2: MSME clients make up an increasing<br>share of banks’ loan accounts<br>Number of lending accounts and share<br>of lending accounts issued to MSME<br>140 45%<br>40%<br>120<br>35%<br>100<br>30%<br>80 25%<br>60 20%<br>15%<br>40<br>10%<br>20<br>5%<br>0 0%<br>% of Accounts are for MSMEs<br>Number of Account of Total Credit<br>Number of Account of Total MSMEs Credit<br>Number of lending accounts (in millions) Jan-12 Dec-12 Nov-13 Oct-14 Sep-15 Aug-16 Jul-17 Jun-18 May-19 Apr-20 Mar-21 Feb-22 % of lending accounts for MSME<br><!-- End of picture text --> Figure 3: Lending volumes have increased over time but not the share of lending to MSME <!-- Start of picture text --> Lending volumes and share<br>of lending to MSME<br> 7 Mio 30%<br> 6 Mio 25%<br> 5 Mio<br>20%<br> 4 Mio<br>15%<br> 3 Mio<br>10%<br> 2 Mio<br> 1 Mio 5%<br> 0 0%<br> % of Loans for MSME<br>Total Loans of Commercial Banks<br>Total MSME Loans of Commercial Banks<br>Total lending (in IDR Billion) % of Loans for MSME<br>Jan-12 Dec-12 Nov-13 Oct-14 Sep-15 Aug-16 Jul-17 Jun-18 May-19 Apr-20 Mar-21 Feb-22<br><!-- End of picture text --> Source: BI Source: OJK Improving access to finance for MSMEs is part of the target outcomes under the economic transformation pillar of Indonesia’