rafmacalaba/data-use-mentions-extended huggingface dataset:
Finally, it is possible to attempt to measure the technological spillovers channel directly: where _ait_ is a measure of technological spillovers by country _i_ at time _t_ , and _At_ =<sup>�</sup><sup>_N_</sup> _j_<sup>_ajt_is</sup> technological spillovers for the world as a whole. By and large, _ait_ is not directly observable. Nonetheless, it can be proxied by indicators such as total citations for a country’s patents by foreigners, or more crudely by the total number of approved patents held by a given country (Hall, Jaffe & Trajtenberg 2001), or the total number of scientific articles published by residents of a given country.

# **3.2 Data sources and adjustments**

We take the measures (1)–(5) to the data, drawing on long historical GDP data from Maddison (2003), and modern data from a combination of several alternative databases. The former dataset spans 1–2001, but since only output data are available, with substantial gaps, it is only used for the computation of (1) (and for illustrative purposes rather than formal analysis). The latter measures draw on data from the World Bank’s World Development Indicators (output data) and Global Migration Database (migration data), the IMF’s International Financial Statistics (financial data) and Direction of Trade Statistics (trade data), and an esoteric mix of science and technology databases, such as the WIPO Patentscope database and the NSF science and engineering indicators (technology data). These were then merged with a series of control variables obtained, among other sources, the WDI and IFS. These are described in detail in the technical appendix.

There are two possible candidate measures for the growth rate _gy_ . The most straightforward measure, which we apply as our benchmark, is the real growth rate, measured in constant 2000 U.S. dollars. However, over longer periods of time
informally employed (62.9 percent). When looking across sectors of economic activity, the potentially affected workers were more likely to be in commerce and services, regardless of age. Figure 11 shows these results.

**Figure 11. The decomposition of workers with earnings between the current and the proposed minimum wage, aged 15-64**

<!-- Start of picture text --> a. By gender b. By educational level<br>35-64 74.40 25.60 35-64 38.78 54.97 6.25<br>15-34 10.86 72.50 16.65<br>15-34 71.48 28.52<br>Male Female Primary or less Secondary Post-secondary<br>c. By sector (public vs private) d. By informality status<br>35-64 81.20 18.47 35-64 52.75 47.25<br>15-34 94.20 4.71 15-34 37.11 62.89<br>Private Public Formal Informal<br>e. By sector of economic activity f. By occupation<br>35-64 10.80 25.82 29.06 35-64 35.15 43.74<br>15-34 13.03 57.32 12.44 15-34 50.42 26.63<br>Agriculture Mining Senior officials Professionals<br>Manufacturing Public utilities Technicians Clerks<br>Construction Commerce Service and market sales workers Skilled agricultural<br>Transport and Comnunications Financial and Business Services Craft workers Machine operators<br>Public Administration Other Services, Unspecified Elementary occupations Armed forces<br><!-- End of picture text --> _Source: Own estimates using 2018 Kosovo LFS._ 19
shrunk slightly, to 6.3 million lenders, and although it remains very large, it no longer stands out, because most of the other major microfinance institutions have continued to expand. This raises a very basic question, which we return to below, which is why, unlike other microfinance institutions, the VF essentially stagnated after 2004.

A second measure of the importance of the Village Fund is by looking at the proportion of households for whom at least one member borrowed from it. The monthly time series is shown in Figure 1, and uses information from the Thailand Socio-Economic Surveys of 2002 (when the VF began to operate), 2004, and 2006-2010; there were no SES surveys in 2003 or 2005. Participation in the VF peaked in 2004, at about one household in three, and stabilized at approximately 30 percent of households thereafter. Participation is substantially higher in rural areas (roughly 40 percent) than in urban areas (about 10 percent).

<!-- Start of picture text --> 0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>2002 2004 2006 2007 2008 2009 2010<br>Proportion of households borrowing from VF<br><!-- End of picture text -->

**Figure 1.  Proportion of Households Borrowing From Village Fund: Rural (top), National (dashed), and Urban (bottom).**

_Source:_ Thailand Socio-Economic Surveys of 2002, 2004, and 2006 through 2010. Note: The outlier in mid-2010 is based on an unusually small sample, and cannot be considered to be representative.

Some further information on the evolution of the VF is provided in Table 2. Households appear to be serial borrowers from the Village Fund, with most of them borrowing year after year, just one in ten saying that they do not wish to borrow again, and
of the assigned video clips, as objectively recorded by the bot. These variables are instrumented using the random assignment indicators to the treatment groups. The first-stage Wald F-statistic is reported. * p < 0.10, ** p < 0.05, *** p < 0.01 insignificant for treatment 1.<sup>31</sup> This unintended effect may potentially be explained by the awareness goal of the docuseries: by showcasing how prevalent VAW is in Indian society, users may have inferred this was also the case in their social circles. Finally, Annex Table A15 shows that quantitatively similar coefficients are obtained when using indexes constructed with principal component analysis.

> 31Annex Table A9 reports impacts on individual items that make up the social norms index. It shows that an increased number of individuals in treatment 2 believed that their Facebook friends were against pre-marital sex for men, and that their friends would condone domestic violence when wives were either unfaithful or went out without permission of their husbands.

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whereas municipalities like _Almenara_ and _Taiobeiras_ procured over 60% from local suppliers.

An important source of heterogeneity discussed in the public procurement literature, which might also affect the identity of suppliers, is the modality of purchase. In general, purchases can be divided into competitive and non-competitive tenders, as described in Table A.2. The literature showing the effects of discretion on procurement outcomes documents that it may have potential negative impacts on efficiency and corruption (Baltrunaite et al., 2021; Decarolis et al., 2020; Palguta and Pertold, 2017). One explanation is related to favoritism, once lack of competition may favor government bureaucracy to engage in opportunistic behavior for private benefit by awarding contracts to local and/or connected firms. These supplier firms may not be the most efficient, leading to overpricing and other inefficiencies. Using Brazilian federal procurement data, Fazio (2022) finds evidence that although public agencies use discretion to purchase higher-quality products, they also use it to favor firms that are politically connected, located in the same municipality as the government agency, larger, and older.<sup>23</sup> In order to investigate this heterogeneity, in Figure 7 we present the distribution of the

> 23In the Hungarian context, Szucs (2023) finds a similar result - winners of high discretion procedures are more likely to be domestically owned. On the other hand, contracts in high discretion procedures tend to be awarded to younger and smaller firms.

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# **Annex A: Overview of variables, definitions, and sources**

|**Definition**|**Description**|**Source**|
|---|---|---|
|Digital platform to participate in<br>decision-making I30(2020 and<br>2022 data)|Are there national platforms that allow citizens to participate in policy decision-<br>making? It takes the value 1 if such a platform exists and 0 otherwise.|GovTech Maturity<br>Index (GTMI) 2022|
|A digital platform to participate<br>in the enhancement of the<br>quality of public service delivery<br>I31(2020 and 2022 data)|Are there government platforms that allow citizens to provide feedback (e.g.,<br>compliments, complaints, suggestions, info requests) on service delivery? This<br>captures the availability of a platform that allows citizens to provide feedback, make<br>suggestions, collect information, or complain about the delivery of public services. It<br>also takes 1 if such aplatform exists and 0 otherwise.|GovTech Maturity<br>Index (GTMI) 2022|
|GovTech (2020 and 2022 data)|This variable indicates whether at least one of I30 or I31 exists for the country. It<br>takes 1 if at least I30 or I31 exists and 0 otherwise.|Authors’ computation<br>based on GTMI 2022|
|Civil Society Participation|It measures the level of civil society participation in policymaking and political<br>processes, with a focus on major Civil Society Organizations (CSOs) being routinely<br>consulted by policymakers, the level of people's involvement in CSOs, the extent to<br>which women participate, and the process for nominating legislative candidates<br>within party organizations. The CSPI ranges from 0 to 1 and can be transformed into<br
It is beyond the scope of this study to discuss the effectiveness and adequacy of countries’ surveys to measure remittance flows. This is a topic that needs to be analyzed separately. However, it is important to note that because most countries do not measure remittances that occur through informal channels, it is not possible to estimate the total value of remittances that developing countries receive every year through those means. Moreover, because in most countries not even an estimate of this type of flow is incorporated in balance of payments statistics, the value of workers’ remittances is probably under-reported.

Although some efforts are currently underway in some of the countries covered in the survey to improve remittance statistics (see Annex 3), much more effort is required. National statistics on remittances are expected to provide comprehensive data on the value and composition of remittances that countries receive every year, covering all different types of instruments and channels through which migrants send money to their home countries. Moreover, statistics on remittances are important for the formulation of macroeconomic policy, especially for countries with a large part of their population living abroad, because large remittance inflows can affect the level of savings, investment and consumption. Remittance data also constitutes a basic instrument for government and non-government agencies that seek to improve the living conditions of senders and beneficiaries of incoming remittances. Furthermore, data on remittances constitutes an important tool to monitor crossborder flows of money and timely detect money-laundering or other illicit activities that may undermine the integrity of a financial system.

Recently, thanks to major improvements in the way remittance data is collected, some developing countries (e.g. Mexico) are starting to realize that the value of remittances they receive every year is actually much more than they had previously thought (see Box 1 below). Moreover, better remittance data is encouraging authorities in developing countries to develop new policies to maximize the developmental impact of those flows by increasing the availability of savings and investment instruments for senders and recipients of remittances. For instance, in some countries in Latin America
**Figure 8 - Decomposition of the change in GHG emissions (tn.) per worker by sector in LAC, 2005-2018**

<!-- Start of picture text --> 2000<br>1500<br>1000<br>500<br>0<br>-500<br>-1000<br>-1500<br>-2000<br>-2500<br>Non-green sectors Green sectors<br>Within Between (structural change) Total<br>Education All sectors<br>defence<br>and fishing communications<br>GHG emissions (tonnes) per worker Financial intermediation Mining and quarrying Transport, storage and as employers Health and social work Hotels and restaurants Real estate, renting and business activities<br>Industrial processes personal service activities Public administration and Wholesale and retail trade<br>Agriculture, hunting, forestry Electricity, gas and water supply Manufacturing, Construction & Other community, social and Activities of private households<br><!-- End of picture text --> Source: Own elaboration based on Climate watch data (GHG emissions in tons) and SEDLAC (Number of workers). Notes: (1) LAC region is composed of the 7 countries with emissions data: Argentina, Brazil, Chile, Colombia, Costa Rica, Mexico, and Peru. (2) In Mexico, data from 2006 were used instead of 2005 due to the lack of estimates of the number of workers. For the same reason, data from years 2006 and 2017 were used in Chile instead of 2005 and 2018. This decomposition follows the traditional shift-share approach from structural transformation studies, where changes in labor productivity are decomposed into productivity growth within the sector (“within” component) and productivity growth driven by workers moving from lower to higher productivity sectors (“between” or “structural change” component).

# Profile of green job holders in LAC

Overall, green occupations are more prevalent among male and urban workers (see
captures perceptions of the extent to which agents in the borrower country have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property

> 10 <mark>Since the last available data are for 2007, for subsequent years we take the values of this year.</mark>

> 11 <mark>This variable is from the World Bank’s Doing Business database. The World Bank changed the methodology of measuring the Information Sharing Index in 2015. Until then, the index ranged between 0 and 6. Since the revised index ranges between 0 and 8, we multiply the post-2014 values of the index by 6/8. The first available year is 2004, and for prior years we take the values for this year.</mark>

> 12  This variable is from the World Bank’s Doing Business database. The first available year is 2004, and for prior years we take the values for this year.

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punctuated by large annual variations, and often failed to sustain. Thus, growth has not just accelerated post-liberalization, but has also become more stable.

**Figure 3: India’s long-term growth rate has become increasingly more stable**

<!-- Start of picture text --> 3 A: Coefficient of variation, GDP growth, 3 B: Coefficient of variation, GDP growth,<br>India EM7 median<br>10-year rolling coefficient of variation 10-year rolling coefficient of variation<br>160 160<br>140 140<br>120 120<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016<br><!-- End of picture text --> Note: World Development Indicators data. Coefficient of variation is calculated as the standard deviation divided by the mean for rolling 10-year periods. For EM7, it is the median of the cross-country series for every year.

# **2.2 Spatial Trends in growth**

Next, we examine growth patterns at the state level.<sup>12</sup> For this we estimate the trend coefficient of Gross State Domestic Product (GSDP) growth for annual data since 1981. We use the following regression equation, where the subscripts _s_ and _t_ denote variation at the state and year level, respectively: GSDP Growth�� �β� �β�Trend� �γ� �ε�� �equation 2� To control for level differences in growth across states, we include state-level fixed effects in the regressions, denoted by γ�. A positive and significant coefficient estimate for β� presents evidence of growth acceleration. We also allow the trend coefficient to vary by state characteristics (equation 3). The characteristics we consider are
lines experienced changes in workers’ informality rates: where _yi,t_ is one of the outcomes of interest of census tract _i_ at moment _t_ . I estimate equation 3.3 for the following outcomes: the share of informal workers and the share of informal firms; T _i_ is one of the four different treatment variables; _δi_ are census tract fixed effects, and _δs_ ( _i_ ) _,t_ are state-time or municipality-time specific trends, _γt · Xi_ are census-tract characteristics-time-specific trends that include the distance controls, _εi,t_ , is the error term of the regression. The coefficients of interest are the parameters _βτ_ , and the baseline year is 1994. Since the line was built in 2000, the placebo for parallel trends corresponds to 1999. I compute the standard errors with clusters at the census-tract level.

Figure 4 and table B4 in the Online Appendix report the point estimates for the main outcome, the share of informal workers. I find that workers’ informality rates decrease in locations near line B after the transit shock. I also find evidence of parallel trends since the point estimate is small and not significant in 1999. On average, informality rates decrease between 2.0 and 4.0 percentage points in locations that experienced the shock. The results are similar using the standard definition of informality or a stricter definition of informality that considers only informal and non-salaried workers that do not have an actual contract with the establishment. Moreover, these effects are robust to the use of the Euclidean distance, the walking distance using the network of roads, or 13
groups or influential families.

We consider distress and closu:re during the two years following the onset of the East Asian financial crisis, i.e., from July 1997 up to July 1999, and treat financial distress and closure separately. To identify distressed and closed financial iinstitutions we rely on publicly available sources, including Central Bank's web sites and newspapers articles. We define distress as all those instances in which a financial institution has received external support as well as when it was directly closed. Distress is identified as one of the following: i) the financial institution was closed; ii) the financial institution was merged with another financial institution;'<sup>0</sup> iii) the financial institution was recapitalized by either the Central Bank, the Deposit Insurance Corporation, or an agency specifically created to tackle the crisis;"1 iv) the financial institution's operations were temporarily suspended. Closure is a subset of distress and includes only the _de jure_ failures.

7 No foreign commercial bank is included in the BANKSTAT sample for Thailand.

8 The 20% cutoff-level has been used by several papers, including La Porta et al. (1999), and appears a robust threshold for a single shareholder to establish the effective control over an institution.

9 Using public information only usually underestimates the number of truly distressed cases. We conjecture that this was less so for our sample of countries during this period. Intervention by the IMF, the World Bank and other agencies and the general scrutiny of international markets meant that weak financial institutions were more likely identified as such during this period. Regardlkss, underreporting of distressed financial institutions would create a bias against finding significant results on out logit estimations. '0 Banks merged under distressed conditions are treated as failed and added to the failed group. i These include the Financial Restructuring Authority (FRA) in Thailand, the Indonesian Banking Restructuring Agency (IBRA), and Danaharta in Malaysia.

7
Figure 3: Effects of average plot size in adjacency <!-- Start of picture text --> 0 1 2<br>log average plot size in adjacency<br>Borewell density<br>Groundwater transaction intensity<br>1<br>.8<br>.6<br>.4<br>.2<br><!-- End of picture text --> _Notes:_ Nonparametric regressions of borewell density and groundwater transactions per plot, respectively, on log average plot size for all 2306 reference borewell plot adjacencies.

were acquired through inheritance.

Land fragmentation would be irrelevant, of course, were groundwater markets frictionless. In this case, Coasian reasoning suggests that allocations should be independent of asset ownership. Thus, borewells would be just as likely on small plots as on large plots; the owner of a small plot could simply sell any excess groundwater to a neighbor. Obversely, small plots would be just as likely cultivated in the dry season as large plots; any plot owner without a borewell of his own could purchase groundwater from that of a neighbor. In Appendix B, however, we use plot-level data from our sample to strongly reject both implications of frictionless groundwater markets. Thus, in practice, land fragmentation does predict the distribution of borewells and fallowed area.

When we aggregate these data from around 9600 plots to the adjacency level in Figure 3, two key facts emerge: First, borewell density–measured as the ratio of borewell irrigated area to total area in the adjacency–is increasing in average adjacency plot area. Put another way, in more fragmented adjacencies, borewell density is lower; this simply reflects the finding in Appendix B that borewells are much less likely to be installed on small plots. Second, the proportion of plots in the adjacency receiving any groundwater transfer from the reference well (aside from transfers between its co-owners) is decreasing in average adjacency plot area. So, borewell density and groundwater market activity are _substitutes_ , both
DOI: 10.1080/00036846.2020.1726865

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