rafmacalaba/data-use-mentions-extended huggingface dataset:
This paper aims to assess whether there is a feasible design for a two-sided market-based solution to temporary migration that would attain a basic income in the U.S. similar to current UBI proposals, such as Yang’s $1,000 a month. The paper confines attention to migration from Mexico. We envisage a market that would essentially eliminate illegal (undocumented) migration, though existing channels for permanent residence of non-citizens (”green cards”) would remain. The number of illegal migrants in the U.S. is not known with certainty, with estimates (reviewed later) in the range 11-22 million, of which around half are from Mexico. The simulations assess whether mutually beneficial trades could simultaneously provide a BIG in the U.S. and earnings gains to Mexican migrants and increased remittances to their families in Mexico. To do this, we use national surveys for 2018 to predict the expected wages of Mexican workers in the U.S., based on Mincer-type earnings regressions estimated on the U.S. survey data and the characteristics of the Mexican worker. We simulate the market when adult citizens of the U.S. can rent out their WPs,<sup>5</sup> and citizens of Mexico are allowed to purchase a yearlong WP in the U.S.

Our “benchmark” calculations restrict eligibility to those currently in the U.S. workforce. However, it can be argued that such a policy will attract citizens currently outside the workforce, to take advantage of the scope for selling their WP. We also study impacts at this extensive margin. Additionally, we consider a more restricted set of eligible Americans, namely those with dependent elderly and/or young (pre-school) children.

Armed with these data and methods, we assess whether, even when the policy is restricted to migrants from Mexico, it is possible to finance a reasonable basic income for Americans by tapping into the economic gains from migration. Costs of migrations and policy restrictions on migration create large
Yes|Yes|
|Observations|6627|6627|4317|4309|
|_R_<sup>2</sup>|0.483|0.493|0.487|0.491|
|Shea’s R-squared|0.173|0.186|0.185|0.182|
|Shea’s R-squared|0.118|0.187|0.117|0.176|
|_J_|0.238|0.0589|0.0376|0.00309|

Note: This table presents the 2SLS regression results. Exclusion restrictions are the regional share of FTM and female ownership and the interaction of each variable with the technology indexes. All estimates control for capital stock, employment, sector, country, and additional controls, including formality and the management human capita index. FTM = Female Top Manager, GBF EXT = General Business Function Extensive Margin, GBF INT = General Business Function Intensive Margin, SBF EXT = General Business Function Extensive Margin, SBF INT = General Business Function Intensive Margin. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Estimates were performed using sample weights. Robust standard errors are in parentheses.

Figure 3 shows the marginal effects of technology on labor productivity by different levels of technology sophistication for women and men-managed businesses based on our 2SLS estimates. Panel (a) displays the marginal effects associated with the sophistication of GBF technologies, while Panel (b) shows the impact of the sophistication of SBF technologies. Our findings indicate that women-managed businesses tend to experience lower productivity returns from technologies than men-managed businesses when technology sophistication is 21
processing trade regime. Under this regime, firms are granted duty exemptions on imported raw materials and other inputs _as long as they are used solely for export purposes_ . Since imported processing inputs may not be consumed domestically, the processing trade data provides, for each processing location, a unique mapping of the source countries where processing inputs are imported from and the destination countries of processed exports. This makes it possible to directly link imported inputs to exported products within the same GPNs, without relying on the proportionality assumption. Furthermore, since the processing trade data incorporate both intra-firm and arm’s length trade, it provides a more complete measure of trade flows within GPNs. In the next section, we provide an overview of the processing trade data and identify three stylized facts that relate import and export distance to processing trade patterns.

# **3. China’s Processing Trade Regime**

China’s processing trade regime was installed in the mid-eighties in order to both attract foreign direct investment and promote exports. Under the regime, firms were granted duty exemptions on imported raw materials and other inputs _as long as they are used solely for export purposes_ . Largely ignored by many scholars, the regime was much more far-reaching than similar systems introduced in other East Asian economies. Unlike in its neighboring countries, China’s concessionary provisions were not geographically limited within strictly policed export processing zones, but rather applied over its entire territory (Naughton, 2006). As a result, China’s processing trade regime has turned into an important part of its overall trade performance. As it is illustrated in Figure 1, between 1988 and 2008, the share of processing exports (i.e. exports conducted under the processing regime) in China's total exports has risen from 30% to 51%, while the share of processing imports in total imports has increased from 27% to 38%.

## **[Figure 1 about here]**

A special characteristic of China’s processing exports is that
of these components from Table Al of Appendix 1, the paper calculates the production of SMIs, while the production of large industries is calculated by using the remaining components and their corresponding weights.

Figures 14 and 15 illustrate the evolution of the constructed series for the production of SMIs and large manufacturing firms (LMFs). It is clear that the downward trend in production is more prominent in the case of SMIs compared to that of LMFs. Indeed, the growth rate in the index averaged over 1997 and 1998 for SMI production is almost -1.4 percent, while that of large industries is around 1.8 percent.

<!-- Start of picture text --> Figure 14. Index of SMI Industrial Production Figure 15. Index of Large Industries Industrial Production<br>160. 193<br>180-<br>150L<br>1 70-<br>140-<br>100<br>130-<br>150-<br>120-10<br>110. ___________________________ 130.___________________________<br>9g :01 9d:04 9607 96:1O 9901 97h04 99:07 97:10 9B8&1 bd:04 98:07 98:10 9 :01 9604 9B:07 9s:1o 99:01 99:04 99:07 W9A:1 98:01 98:047 901O<br><!-- End of picture text --> Table 2 also presents descriptive statistics of respective index of SMIs and large industries between 1992:01 and 1998:12. It is interesting to note that the variability of SMIs' production is almost twice as much as that of large industries, as demonstrated by the coefficient variation. A possible explanation for this phenomenon is provided by Gertler and Gilchirst (1994) who

> 21 A recent study by Domac and **Ferri** (1998) on Korea concluded that SMEs suffer more than other businesses do from the adverse ceffects of the credit channel.

19
a state should do about them.

> 12 OCAP comprises some 50 items and so to produce a short form version, Lorgelly et al (2015) developed a consultation process based on focus group interviews. The refinement developed subsequently by Simon et al (2013) drew also on this shortened version produced by Lorgelly and colleagues and produced psychometric data for capability indicators in Vergunst et al (2017). 13 It is worth noting that this approach could be seen as an alternative to those studies that follow a tradition in development of measuring empowerment in terms of agency – see for example recent work by Alkire et al (2013). Our approach helps to identify the areas of life in which a person’s capabilities have changed and therefore offers a complement to measures of agency or empowerment that focus on delivering an overall judgement about whether someone is empowered or otherwise.

13
Figure 1 shows the AGEBs and municipalities that appear in the main sample. Panel A shows AGEBs, with individual AGEBs colored based on whether they are in the estimation sample (“Yes”), are not in our estimation sample but are urban (“No”), or are rural AGEBs that are not considered in the analysis (“Not in results”). Panel B shows the same, but for municipalities. Specifically, a municipality is coded as included in the sample if at least one AGEB within that municipality is contained in the sample. As such, a municipality in our sample could have a relatively small proportion of its total population actually included in our sample. There are 1,641 municipalities that contain at least one urban-AGEB with non-missing data. Of these, 1,034 – or 63.0 percent – have at least one AGEB in the sample.

# **b. Auxiliary geospatial data**

Auxiliary data is drawn from two sources: Google Earth Engine, and Open StreetMap, utilizing the 2020 shapefile of urban AGEBs provided by INEGI. These sources were largely selected because they are publicly available, cover a large portion of the world, and convenient to obtain. However, both contain a large number of candidate predictors that could be plausibly correlated with spatial patterns in labor force participation. From Google Earth engine, we extracted summary statistics by AGEB from six datasets: Nitrogen Dioxide from Sentinel 5P, Normalized Difference Vegetation Index (NDVI) from the Sentinel 2 Multi-spectral Instrument, Nighttime lights from VIIRS, estimated population from WorldPop, land cover classifications from the Copernicus dynamic land cover map, and the year of development, as proxied for by the change in pixels from pervious to impervious surfaces (Gong et al., 2020). All summary statistics were taken over March 2020, except for land cover, which pertains to the period between January and December 2019.

From Open StreetMap, indicators were obtained representing the total length and number of highways in each AGEB. Two measures of
was undertaken during the 51<sup>st</sup> round of the NSSO. Subsequently, surveys of manufacturing enterprises in the unorganized sector were conducted in the 56<sup>th</sup> (2000-01), 62<sup>nd</sup> (2005-06), and 67<sup>th</sup> (201011) rounds.<sup>29</sup> 29 Thus, the surveys on unorganized manufacturing enterprises usually covered: (i) manufacturing enterprises not registered under Sections 2m(i) and 2m(ii) of the Factories Act, 1948; (ii) manufacturing enterprises registered under Section 85 of the Factories Act, 1948; (iii) non-ASI enterprises engaged in cotton ginning, cleaning, and baling (NIC2004, code 01405); and (iv) non-ASI enterprises manufacturing bidi and cigar (those registered under the Bidi and Cigar Workers (condition of employment) Act, 1966, as well as those un-registered). Some of the surveys (such as 2010-11) covered trading enterprises and services establishments, but we exclude them for the purposes of our paper. Additionally, the NSSO excludes: (i) repairing enterprises not falling under Section ‘D’ of NIC-2004; (ii) departmental units such as railway workshops, RTC workshops, government mint, sanitary, water supply, gas, storage, etc. in line with ASI coverage; (iii) units covered under ASI; and (iv) public sector units. The sample design for the unorganized sector is a stratified multi-stage design. The first stage units (FSU) are the census villages in the rural sector and Urban Frame Survey (UFS) blocks in the urban sector. The ultimate stage units are enterprises in both sectors. In the case of large FSUs, one intermediate stage of sampling is the selection of three hamlet-groups/sub-blocks from each large 12
rural area. In each wave, the household location information is available in book K. We match the household ID with the location information.

**Permanent income:** We use the household expenditures as a proxy for income, since income data often suffer from more measurement error than household expenditure. For the fathers’ generation, we use the average of household consumption expenditure of the first three waves, and the children’s generation, we the average of household consumption expenditure of the last two waves. Below we discuss in more details how we calculate the permanent income:

# **Fathers’ generation permanent income:**

For each of the first three waves, we first separately calculate the household expenditure. For each household member, the IFLS provides his/her relationship to household head. We identify the households in which the individuals of our children sample (18 to 40 years old in 2014) were listed as children or grandchildren in the any of the first three waves. We consider both children and grandchildren because these children may grow up in an extended family where the grandfather is household head. Using the household tracking id, we then merge the identified households with their total expenditures. We adjust for inflation by using yearly inflation data from the World bank web site taking 1993 as the base year. After this we merge the sample with fathers’ age information. We restrict the sample to fathers who are age 25 to 56 in 1993.

# **Children’s Generation Permanent Income:**

For each of the last two waves, we first calculate the household expenditure. Since we want to focus on children who are 25 to 56 years old in 2007, we extend the original children sample to this age group. We then identify children who are household heads in 2007. We wanted to make sure the chil4
Because the between-group inequality component includes population weights, the contribution to overall GE(0) inequality has been much smaller for Zambia than Nigeria. Among the largest changes, DRC, Mozambique and Tanzania increased their contribution to total inequality, while Madagascar, Nigeria and Zambia moved in the opposite direction. Angola changed from a positive to a negative contribution, its mean thus moving across the regional mean. Given that the revision of the PPP exchange rate hardly affected the overall level of inequality, the negative and positive effects on the between component largely cancelled out.

Instead of considering a specific measure of inequality, Table A2 looks at the entire African distribution. Each decile of the 2008 African distribution was split according to the population contribution from each country (similar to Figure 9). The table shows the change in these population contributions as a result of the change in PPP exchange rates. As a result of the new PPP exchange rates, DRC increased its population share in the bottom decile by 12.1 percentage points (from 37% to 49% of the African population in the poorest decile). In contrast, the share of Mozambique in the eighth African decile decreased by 1.8 percentage points (from 3.3% to 1.5%). Given its population size, Nigeria had the largest upward movement, while Angola, Madagascar, Sudan and Zambia moved in the same direction. On the other hand, DRC, Mozambique, Tanzania and South Africa have reduced their shares in higher percentiles.

> 54 In other words, the between-component in the GE(0) decomposition relies on log-differences in mean consumption, not differences in ranks. Furthermore, we use population weights throughout, so the size of the country obviously matters.

26
? We test the quality of our data by generating aggregates from our microdata and comparing them with information from the Brazilian Public Sector Accounting and Tax Information System ( _Sistema de Informações Contábeis e Fiscais do Setor Público Brasileiro_ , SICONFI), a dataset maintained by the National Treasury to “facilitate the production and analysis of accounting and tax information, standardize consolidation mechanisms and increase the quality and reliability of accounting information, financial and fiscal statistics received from municipalities, states, the Federal District and the central government.” We show that our aggregates closely match those of the SICONFI, not only at the municipalityyear level but often at the more disaggregated level of function, which is a classification that groups expenditures according to their purpose (e.g., health, education, security).

We exemplify possible applications of our data by exploring two features of municipal public finances. First, we evaluate the extent to which local governments buy from suppliers located in the same municipalities. García-Santana and Santamaría (2023) show compelling evidence of home bias in the European Union and discuss its implications for the value-formoney of public procurement. We show how our microdata allow us to identify each gov-

> 4 Available as the _Microdados de Despesas de Entes Subnacionais_ (MiDES) dataset at https://basedosdados.org/dataset/d3874769-bcbd-4ece-a38a-157ba1021514.

> 5In Section A.1, we illustrate how the dataset can be seamlessly queried to perform descriptive analyses using R.

> 6Available at https://github.com/basedosdados.

3
trade is expected to be _positive_ . Theoretically, Kletzer and Bardhan (1987) argue that countries with better developed financial sectors have higher exports from sectors that rely more on external financing, holding all else constant. If financial sector development reduces the costs of external finance to firms, then countries with more developed financial institutions will specialize in sectors that rely more on external finance. Comparative advantage through financing can be important for trade by facilitating risk diversification, providing finance to sectors in need, or allowing the acquisition of improved technology to enhance productivity.

The impact of the interaction between asset tangibility and trade specialization is expected to be _negative_ . The higher a sector’s level of asset tangibility the more it facilitates collateralization and liquidity in markets where default risks are high or when contract enforcement is weak (Braun, 2003). If higher asset tangibility facilitates lending when contract enforcement is weak or when default risk is high, then countries with a more (less) developed governance or financial sector institutions should have lower (higher) export shares in industries that use more tangible assets.

|��������|�������|Predicted Effect onto<br>Trade Specialization|
|---|---|---|
|Financial Sector Development|External Finance Dependence|+|
|Financial Sector Development|Asset Tangibility|-|
|Governance|AssetTangibility|-|

> 17 Specifically, industries are identified by the 2-digit harmonized systems product classification code (http://www.foreigntrade.com/reference/hscode.htm).

> 18 For example, see La Porta et al 1999; Rodrik, Subramanian, Trebbi, 2002

> 19 See Berg and Kruegter, 2003; Islam and Montenegro, 2002; Wei, 2000

10
an increase in non-performing loans (NPLs), which may weaken both financial stability as well as the sector’s capacity to contribute to the post-pandemic economic recovery. Asset quality could deteriorate significantly in the wake of the pandemic outbreak, especially in countries where firms and households exhibit high levels of debt (Reinhart, 2021b).

Financial sector authorities undertook a wide-ranging set of measures to preserve the stability and resilience of the banking system and bolster its capacity to support economic activity (Demirgüç-Kunt et al., 2020; Feyen et al., 2020 **;** Casanova et al., 2021). Some of these measures may have reduced the transparency of bank balance sheets.

To explore the value added of CDBP in environments where financial data are limited or opaque such as it is the case for many economies during the COVID-19 pandemic, we collect balance-sheet information for Q3 2020 for approximately 1,500 commercial banks headquartered in 59 EMDEs<sup>11</sup> using Fitch data. If balance-sheet information is not available for the third quarter of the year 2020, we use the latest balance sheet information available, although not older than 2019.<sup>12</sup> The key variables needed to compute the Consolidated Distance to Breakpoint (CDBP) are:

- NPL ratio (as % gross loans);

- Total Regulatory Capital (in USD millions);

- Risk-weighted Assets (in USD millions);

- Minimum Capital Requirement (as % of RWA);

- Gross Loans (in USD millions);

- Total Assets (in USD millions).

Figure 2 summarizes the country distribution of the CDBP across World Bank regions. In line with recent assessments (see for example Yuuki et al., 2021), the data suggest that EMDE banks are resilient in the short-term to credit shocks, although there is wide variation across regions and countries. The country median CDBP is 9.8 percentage points (pp). This percentage-point increase in the consolidated NPL ratio of “Banks at
.233|10.040|
||Upper|4.418|5.823|8.634|12.450|14.859|
||Lower|1.807|2.610|4.217|6.024|7.630|

_Notes:_ Data on simulated temperature are from the Coupled Model Intercomparison Project (CMIP6) climate projections. The projection is estimated using the coefficient on the effects of temperature on inequality reported in Columns (2), (4), and (6) (Panel B) of Table 1.

**_Panel B: Inequality_**

|||SSP1-1.9|SSP1-2.6|SSP2-4.5|SSP3-7.0|SSP5-8.5|
|---|---|---|---|---|---|---|
|Temperature|Mean|1.400|2.000|3.000|4.100|5.000|
||Upper|2.200|2.900|4.300|6.200|7.400|
||Lower|0.900|1.300|2.100|3.000|3.800|
|Gini index|Mean|0.489|0.698|1.047|1.431|1.745|
||Upper|0.768|1.012|1.501|2.164|2.583|
||Lower|0.314|0.454|0.733|1.047|1.326|
|Theil index|Mean|0.8288|1.184|1.776|2.4272|2.96|
||Upper|1.3024|1.7168|2.5456|3.6704|4.3808|
||Lower|0.5328|0.7696|1.2432
As a result of these mergers and exclusions, the number of administrative units we retained as the universe of places for the analysis fell to 564,052.

Two concerns can be raised on the use of such administrative units as the unit of analysis. The first one, known as the modifiable areal unit problem (MAUP), is the potential sensitivity of results to their considerable diversity in shape and size. It has been shown that shape does not make much of a difference for three important topics in the economic geography literature (namely, spatial concentration, agglomeration economies, and inter-regional trade). As for size, administrative boundaries do not suffer significantly from the MAUP, except in the case of large-scale spatial units, such as states or provinces (Briant, Combes and Lafourcade 2010). However, none of the administrative units considered here reaches such large scale.

A second concern is whether administrative units, self-standing or merged in the case of bigger cities, provide an accurate representation of integrated labor markets. An alternative is to delineate the urban extent by aggregating contiguous cells. Depending on the studies, the aggregation of cells is based on criteria such as the density of their built-up or the intensity of their nighttime light (Baragwanath et al. 2019; Ch et al. 2018; Rozenfeld et al. 2011). However, the nature of the links between cells in a broader spatial context may vary across localities. The aggregation process can introduce other biases (Bosker et al. 2018).

For selecting a representative sample, we further restricted the set of administrative units to those of the 21 largest states and union territories (hereafter states). These large states are covered by the high-quality household surveys whose data are needed for a proper stratification. Focusing exclusively on them resulted in 561,624 places being considered for sampling.

The total population of these 561,624 polygons in 2011 was 1,174 million, or 3.0 percent less than reported by the 2011 Census of India for the country as