rafmacalaba/data-use-mentions-extended huggingface dataset:
|HH Head is Married|0.678|0.467|0.712|0.453|0.702|0.458|0.647|0.478|
|---|---|---|---|---|---|---|---|---|
|HH Wealth and Education Index<sup>a</sup>|0.375|0.234|0.456|0.224|0.363|0.223|0.367|0.243|
|HH Ag Assets and Land Holdings Index<sup>b</sup>|0.388|0.204|0.484|0.207|0.431|0.224|0.325|0.161|
|**Community Characteristics (2010 values)**|||||||||
|Community infrastructure index|‐0.099|1.078|‐0.609|1.006|0.226|1.049|‐0.271|1.034|
|Community median slope (%)|4.564|3.761|5.142|3.915|4.009|2.674|4.931|4.446|
|ln(Access Index)|4.519|1.434|2.439|1.827|5.455|0.957|4.171|0.900|
|District AEDOs per 1000 farming families|0.433|0.135|0.538|0.173|0.472|0.113|0.372|0.112|
|Total N. Projects in District|4.385|3.567|4.298|1.148|6.294|4.457|2.
|Hungary|14.6|2.6|3.7|4.7|
|---|---|---|---|---|
|Lithuania|15.9|11.1|0.9|4|
|**Latvia**|**10.5**|**5.5**|**1.3**|**3.8**|
|Poland|22.5|12.7|2.7|7.1|
|Romania|25.4|21.9|0.9|2.6|
|Slovenia|11.2|3.8|2.6|4.7|
|Slovakia|7.8|0.4|2.7|4.7|
|CEC-10|19.5|11.3|2.4|5.8|

Source: Eurostat, Employment and labor market in Central Europe 2001/2

**Second jobs play an important role.** The number of part-time jobs in the economy greatly exceeds that of part-time workers. Apart from those whose main job is a part-time one, 7 percent of those employed in 2002 have at least one second job. Second jobs are typically part-time. The prevalence of second jobs is most likely underestimated in the 2002 LFS, because respondents whose information was given by another household member report a second job 1.5 times less frequently than those interviewed directly.

According to an enterprise survey on wages and hours worked, 13 percent of public sector employees had second jobs in 2002. In such sectors as financial intermediation, business, education and other community and personal services, the proportion was much higher. Two thirds of second jobs are in the service sector. The shares of individuals engaged in managerial, professional and technical occupations in secondary jobs are higher that those in primary jobs. Appendix Figure 4
# **Figures**

Figure 1. Women’s Marriage Age/Probability of Women’s Marriage Before Age 18 Post the Gujarat riots of 2002

## (a) Womens Marriage Age

<!-- Start of picture text --> Marriage Year<br>Data Source: NFHS-4<br>(b) Probability of Women’s Marriage Age Before Age 18<br>Marriage Year<br>Data Source: NFHS-4<br>.5<br>0<br>-.5<br>Diff-in-diff Coefficients<br>-1<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>.15<br>.1<br>.05<br>Diff-in-diff Coefficients<br>0<br>-.05<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> _Note_ : This figure plots the difference-in-differences estimates from specification 2 using NFHS-IV. In subfigure (a), the outcome variable is women’s age at marriage. In subfigure (b), the outcome variable is an indicator for women’s marriage before age 18. Standard errors were clustered at the state-year level for both subfigures.

30
and channels of control;

- Institutional framework for oversight of financial systems, including the responsibilities, independence, resources and accountability of the supervisory and regulatory bodies; and the role, liability and funding sources of self regulatory bodies and agencies responsible for due diligence in financial systems (credit rating agencies, accounting firms) -- to help identify conflicts of interest and potential moral hazard;

- Adequacy of financial statistical data and information disclosures on the risk exposures of financial institutions, and the adequacy of the analysis and early warnings based on financial information -- to identify significant information gaps that would weaken market discipline or effective surveillance of financial systems;

- Role of implicit and explicit guarantees in the financial system and the role and effectiveness of crisis management, resolution and bankruptcy provisions, including potential systemically important and too-big-to-fail financial institutions -- to identify potential moral hazard;
: agricultural data zero|1.674*|2.265+|0.740|0.494|0.624|-0.167|0.563|1.777**|1.331+|0.787|1.356|
|Dummy: data at NUTS 2 level|-0.287|0.683|-0.774+|-0.428|0.309|-0.681+|-0.490|-0.657|-0.283|-0.711|-0.893+|
|Constant|22.56**|22.23**|23.49**|24.23**|23.38**|23.61**|23.96**|21.69**|23.33**|24.42**|23.64**|
|Interaction termms with countries|N|N|N|N|N|N|N|N|N|N|N|
|Country fixed effects|Y|Y|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|Observations|1,205|1,205|1,205|1,205|1,205|1,205|1,205|1,205|1,205|1,205|1,205|
|Adj R-squared|0.376|0.376|0.369|0.376|0.391|0.377|0.368|0.376|0.376|0.372|0.373|
|<br>Note: (1) Data source: EU 2011 Poverty Map (DG-REGIO<br>observations in agricultural indicators are treated as zero;|and World Bank), 201<br>(4) Luxembourg and
- _Subjective assessments_ of images provided by Google Earth are used to determine whether the cities, towns and villages in the sample are urban or rural in practice. Google Earth is an integrated collection of processed satellite, aerial, street view, and 3‐D images. Using this combination of perspectives, a layperson can judge the characteristics of a specific location. Google Earth images have been used to crowd‐source judgments on city safety, inequality, and gentrification (Hwang and Sampson 2014; Salesses, Schechtner, and Hildalgo 2013). They have also been used in the remote sensing literature to establish training sites and validation sites at the pixel level (Bontemps and others 2011; Schneider, Friedl, and Potere 2009; Yu and Gong 2012).

- A regression analysis combining _multiple sources of information_ is used to identify the correlates of units in the sample being urban in practice. We georeference population census data, which come from the ground, to all administrative boundaries in India. We also incorporate data from open‐source satellite imagery, which come from outer space, on both built‐up areas and nighttime lights. Bootstrapping and cross‐validation are applied to choose the model specifications with higher prediction accuracy. These model specifications and the estimated coefficients are then used to predict whether each of the other units in the country is urban or rural in practice.

This exercise allows a more accurate assessment of the rate and speed of urbanization in India than was available before. Contrary to frequent claims, we do not find that India is substantially more urban at present than stated by census data. And the speed of urbanization over the last decade is only marginally higher than official statistics suggest. At the same time, however, we find a considerable misclassification of locations in the midrange between villages and state capitals.

The analysis also has implications beyond India. We find that the accuracy of the assessment increases substantially when combining data from the ground and from outer space. In the process, it appears that some of the most
<!-- Start of picture text --> Daily. new confirmed COVID-19 cases per millionwe people -<br>Shown is the rolling 7-day average. The number of confirmed cases is lower than the number of actual cases; the =<br>main reason for that is limited testing.<br>1,200<br>1,000<br>800<br>600<br>Georgia<br>400<br>200<br>dordan<br>0<br>Mar2, 2020 Aug 8,2020 Nov 16,2020 Feb 24, 2021 Sep 12, 2021<br>Source: Johns Hopkins University CSSE COVID-19 Data CC BY<br><!-- End of picture text --> <!-- Start of picture text --> Median age vs. children per woman<br>Theabovemedian the medianage divides as there arethe population with agesin belowtwo parts the median.of equal size:Fertilitythatrateis, representsthere are as themany numberpersons of childrenwith agesthat<br>would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance<br>with age-specific fertility rates of the specified year.<br>Asia<br>40 years (Georgia<br>30 years<br>& Jordan<br>f (C]<br>5 20 years<br>2<br>10 years<br>0 years xSH$\p>s4j>$s$>4+$$a$<br>2 3Children per woman4 5 6<br>Source: UN Population Division (2019); World Bank OurWorldinData.orgage-structure « CC BY<br><!-- End of picture text -->
**Figure 6: Shares of Russian Exports and Export Destination Shares of Global GDP, 2007–12**

<!-- Start of picture text --> 6% Change in share of RUS exports, 2007‐12 Change in share of global GDP, 2007‐12<br>4%<br>2%<br>0%<br>‐2%<br>‐4%<br>‐6%<br>‐8%<br><!-- End of picture text --> Source: COMTRADE and WDI data.

Notes: MENA = Middle East and North Africa; NonCU-CIS = members of the Commonwealth of Independent States, except the original three members of EACU; ROW = Rest of World; EU = European Union.

0
, 2014.

- World Bank, “A Measured Approach to Ending Poverty and Boosting Shared Prosperity: Concepts, Data, and the Twin Goals,” Policy Research Report, The World Bank: Washington DC, 2015.

- World Bank, “Poverty and Shared Prosperity 2016: Taking on Inequality,” The World Bank: Washington, DC, 2016.

World Bank, “Poverty and Shared Prosperity 2018: Piecing Together the Poverty Puzzle”, The World Bank: Washington DC, 2018a.

World Bank, 2018b: Global Shared Prosperity Database. https://datacatalog.worldbank.org/dataset/globaldatabase-shared-prosperity Zeileis, Achim, Torsten Hothorn, and Kurt Hornik. "Model-based recursive partitioning." Journal of Computational and Graphical Statistics 17, no. 2 (2008): 492-514.

22
contact number if available, the language preference of the participant (Arabic, English, Dinka, or Nuer) and the participant’s preferred time of day for calls.

Each month’s SSEPS questionnaire consisted of two modules: a set of core questions that were repeated each month and a set of “special” questions that were changed on a month-to-month basis. The core questions fall into three categories: past changes and outlook, material deprivation, and security. The wording for the core questions was taken from Afrobarometer surveys conducted in 2008 and 2009, in

> 5 Facilities for mobile charging through community charging stations, usually powered by generators or car batteries, are common in urban areas.

9
||Does Village Fund<br>|Borrow to On-lend?<br>|
|---|---|---|
|**Performance measures**|No|Yes|
|Loan recovery rate, %|70.3|84.6|
|Capital available, millions of baht per fund|1.273|2.597|
|Lending, millions of baht per fund|1.103|1.970|
|Profit rate, %|-18.0|0.4|
|Adults borrowing from VF, %|10.4|13.1|
|Social contributions,thousands of baht|6.25|6.85|
|**Credit rating (totals to 100%)**|||
|None/Not known, %|36|17|
|A, %|5|3|
|AA, %|30|36|
|AAA,%|29|44|
|Loans topoor/loans to non-poor(rural areas only)|1.61|1.82|
|Source: Village Fund Survey 2010, and 2009 SES (for fin|al row).||

In an attempt to understand better the phenomenon of VF borrowing to on-lend, we used the data from the 2010 VF survey to estimate a logit model where the dependent variable is set to 1 if the VF borrows to on21
each of these shocks impacts the different components of worker compensation within and across heterogeneous exporters. As Grigsby, Hurst, and Yildirmaz (2021) emphasize, measuring this nominal wage adjustment in micro data is key for disciplining macroeconomic theories of nominal wage rigidity.<sup>9</sup>

> 6Since there are no official forecasts for bilateral exchange rates, it is often difficult to isolate the unexpected component of exchange rate shocks.

> 7Furthermore, exchange rate movements may impact not only firm revenues in export destinations, but also the prices of materials, components and technologies sourced from those markets (which may complement or replace workers). It has been difficult to fully distinguish between these effects. Unexpected demand shocks in destinations are arguably more likely to impact firm performance primarily through exports.

> 8In an important contribution, Hummels, Jorgensen, Munch, and Xiang (2014) examine effects of offshoring and external demand shocks on wages, using employer-employee data from Denmark. Identification of demand shocks exploits variation in firm-specific weighted averages of imports of particular goods by the firm’s trading partners, using the firm’s initial shares as weights. We innovate by isolating the unexpected (and idiosyncratic) component of demand shocks in destinations. In addition, we separately identify effects on the different components of pay, show that wage gains accrue mainly to top earners, and to firms initially led by highly skilled top executives.

> 9In concurrent work, Garin and Silv ́erio (2024) examine the effects of idiosyncratic demand shocks associated with the 2008 global recession on wage setting of Portuguese exporters, and find evidence of significant wage adjustments within firms. The methodology we propose for systematically isolating the unexpected component of macroeconomic demand shocks in destinations can be applied more generally to assess the effects of unforecasted (positive and negative) fluctuations in GDP growth. By design, our approach is arguably less likely to capture

4
Video + Video x Female = 0|0.107|0.024|0.725|
|FDR q-value: Video + Video x Female = 0|0.088|0.034|0.319|
|Av. Dep. Var. (Control group)|0.000|0.000|0.000|
|Observations|828|827|827|

_Notes:_ This table presents OLS estimates derived from Equation 1 with interactions between the treatment exposure indicator variables and the indicator variable for female, denoted as female [=1]. The dependent variables are constructed following the methodology outlined by Kling, Liebman and Katz (2007) for each group of outcome variables: (i) Prosocial Behavior (detailed in Table 2), (ii) Attitudes (detailed in Table 4), and (iii) Beliefs (detailed in Table 5). All columns control for student status. Robust standard errors are reported in parentheses. The reported p-values, such as Game + Game x Female = 0 and Video + Video x Female = 0, represent the p-value of a Wald test for each linear combination. Additionally, the False Discovery Rate (FDR) q-values for Game + Game x Female = 0 and Video + Video x Female = 0 are provided. Significance levels are denoted as<sup>_∗∗∗_</sup> at 1%,<sup>_∗∗_</sup> at 5%, and<sup>_∗_</sup> at 10%.

59
result, valuable investment opportunities go unfunded.

Another source of friction is aversion to risk. While worthwhile on average, education is a risky investment. Debt, which is by far the most widespread credit instrument to finance education, does not transfer much risk away from the student, so students with loans take a substantial amount of risk. As a result, risk-averse individuals will be less likely to take loans. Friedman (1955) proposed an alternative to debt, analogous to the equity investments capitalists make in risky projects. Palacios (2004) analyzes this alternative, which he calls Human Capital Contracts (HCCs). During the last 25 years an increasing amount of income contingent loans, which are equivalent to a HCC with a cap in the payments students make, have been made available by governments and in some few instances, by the market. These instruments transfer risk away from students and thus partially aleviate the problem that students financing their education with loans face.

Debt aversion may be another source of friction in the higher education financing market. If students dislike debt, they might simply not invest as much in their education as they would have if the contract was framed differently. This underinvestment of education will imply lower wages over the rest of their career. Evidence supporting the existence of debt aversion stems from surveys that suggest certain demographic groups are unwilling to finance their education using debt (see Rasmussen (2006) and references therein). Experimental data offers contradictory evidence: Eckel et. al. (2007) find no evidence of debt aversion among Canadian citizens while Field (2009) provides empirical evidence from a field experiment that contracts framed as loans are less attractive for students than a convertible grant (a grant that converts into a loan) even though the financial payoffs are identical. The study of borrowing constraints from the perspective of debt aversion is, to our knowledge, only present in the labor economics literature.

Our work is similar in spirit to that of Eckel et. al. (2007) and Field (
|0.77|0.42|
|Father’s Edu|4.20|4.26|

Notes: The variable _Normalized Inpres Intensity_ measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. The full sample corresponds to children born between 1957 and 1962, or 1968 to 1972. Edu represents years of schooling, which was calculated based on the education level completed. Primary completion takes the value of 1 if the child has completed primary and 0, otherwise. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

38
commerce can help accelerate consumption growth and narrow the regional gaps in e-commerce development, which in turn can lead to a more inclusive spatial development pattern.

Due to data limitations, our study cannot separate total household consumption into online consumption and offline consumption, and therefore cannot examine the effects of e- commerce development on these two types of consumption separately. Studies on whether e- commerce results in additional offline consumption or online consumption substitutes for offline consumption will be interesting future research topics. Of equal if not more importance, studies on how e-commerce contributes to entrepreneurship and employment, and how this varies across sectors, regions, and individuals of different characteristics, will be crucial to understanding the role of e-commerce in the production and income aspects. In what areas and to what extent e- commerce adds to or substitutes for traditional offline business, and as a result, the net effect on the economy remains an important question for more empirical studies.

# **References**

Agriculture and Agri-Food Canada (2017), E-grocery market in China,

- http://www.agr.gc.ca/eng/industry-markets-and-trade/international-agri-food-marketintelligence/asia/market-intelligence/e-grocery-market-in-china/?id=1504037238257.

- Baye, Michael R., and John Morgan, “Price Dispersion in the Lab and on the Internet: Theory and Evidence,” _The RAND Journal of Economics_ 35, no. 3 (2004): 449.

- Baye, Michael R., John Morgan, and Patrick Scholten, “Price Dispersion in the Small and in the Large: Evidence From an Internet Price Comparison Site,” _Journal of Industrial Economics_ 52, no. 4 (2004): 463–496.

- Blum, Bernardo S., and Avi Goldfarb, “Does the Internet Defy the Law of Gravity?,” _Journal of International Economics_ 70, no. 2 (2006): 384–405.

- Brown, Jennifer R., Tanjim Hossain, and John Morgan, “Shrouded Attributes and Information Suppression: Evidence from
Table A3. Contributions of global shocks to alternative measures of global inflation: over time

||Oil Price Shock|Global Supply Shock|Global Demand Shock|Interest Rate Shock|
|---|---|---|---|---|
|||Headline CPI inflati<br>|on<br>||
|1970-1985|20.5|25.1|35.9|18.6|
|1986-2000|19.8|24.8|35.9|19.5|
|2001-2022|43.7|12.5|21.4|22.4|
|||Producer Price Index Inf<br>|lation<br>||
|1970-1985|26.5|18.8|30.3|24.2|
|1986-2000|22.6|14.1|42.2|21.0|
|2001-2022|41.7|14.9|24.6|18.8|
|||Core CPI inflation|||
|1970-1985|23.9|24.7|32.3|19.7|
|1986-2000|5.2|32.5|38.0|24.3|
|2001-2022|29.2|30.6|13.9|26.4|

Note: This table reports the evolution of variance decompositions of three global inflation measures based on median from 1000 successful Bayesian draws. The global FAVAR model that consists of oil price growth, and the global factors of inflation, output, and interest rates extracted from the dataset of G7 countries.

53
.193|0.069**|0.069**|0.000|-0.012|
|x Garments|(0.292)|(0.118)|(0.028)|(0.027)|(0.026)|(0.030)|
|Average|35.810|4.867|12.740|7.699|5.039|0.358|

_Source_ : Author’s calculations from Cambodia SES 2007-2015. _Note_ : The table is identical to Table 2 except that it restricts the observations to those surveyed within 6 quarters of the minimum wage hike. Standard errors, clustered by survey year _×_ psu shown in parentheses. ***: p _<_ 0.01; **: p _<_ 0.05; *: p _<_ 0.1.
Table 5 Regression models to impute consumption into 2010/11 using variables from 2018/19 NLSS

|||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|---|
|VARIABLES|LABEL|Original Model|Model 1|Model 2|Model 3|Model 4|Model 5|
|zone1|North Central|-0.184***|-0.184***|-0.187***|-0.263***|-0.188***|-0.242***|
|_||(0.0140)|(0.0139)|(0.0140)|(0.0134)|(0.0139)|(0.0135)|
|zone_2|North East|<br>-0.415***|<br>-0.411***|<br>-0.392***|<br>-0.473***|<br>-0.412***|<br>-0.474***|
|||(0.0171)|(0.0170)|(0.0166)|(0.0158)|(0.0170)|(0.0166)|
|zone_3|North West|-0.325***|-0.322***|-0.298***|-0.362***|-0.319***|-0.376***|
|||(0.0152)|(0.0151)|(0.0143)|(0.
of hidden debt equals the total amount of hidden debt revelations for any particular country.

**Interpretation:** As explained above, the data from the World Bank International Debt Statistics that we analyze is exclusively based on debtor reporting. It is not subject to valuation changes and is compiled according to reporting rules that have been remarkably stable across 50 years. Therefore, revisions should be exclusively caused by changes in

> 17Note that equation (2) has a clearer interpretation for the flow measure than for the stock measure of debt. The reason is that an ex post addition of a single _loan_ leads to revisions of debt _stocks_ across all years until final repayment and therefore involves double-counting. The same issue does not exist for the flow measure.

11
freedom of association.

A factory’s compliance findings remain on the transparency portal until a new Better Work assessment report is published, at which point the site is updated to reflect just the most recent data. The transparency portal is updated continuously and includes information on factory name, factory type, country, assessment date, cycle number and the number of publicly reported issues found not to comply with international labor standards or national law. The findings are published in English.

The 26 critical issues of the Better Work Vietnam’s Public Disclosure Programme were approved and announced to all factories in August 2015. The program went into implementation in June 2016, and in April 2017 the first compliance reports within the 26 critical issues were made publicly available when the transparency portal went live.

In our analysis, however, we use the full set of data collected in the Compliance Synthesis Report during unannounced visits where monitoring teams of usually two people carry out an assessment of working conditions in participating factories. This data set is available from 2010 through mid‐2018 and includes information on all compliance points, including the 26 critical issues. Data from the Compliance Synthesis Report are subsequently merged with Better Work Vietnam’s Registration Document, which contains

> 2 The transparency portal is available at <u>https://portal.betterwork.org/transparency.</u>

3