rafmacalaba/data-use-mentions-extended huggingface dataset:
dimension of the availability of information and information quality is the information sharing that occurs between lenders through credit bureaus. Information sharing via credit bureaus has been shown to increase the competitiveness in the credit market (Vives, 1990), increase in the volume and efficiency of lending (Pagano and Jappelli, 1993) and lower default rates.

The empirical evidence overwhelming supports the positive impact of having credit bureaus. Jappelli and Pagano (2002) and <mark>Djankov, McLiesh, and Shleifer (2007) c</mark> ollect data on the existence and operation of <mark>credit bureaus around the world and find that bank lending is higher and credit risk is lower in countries where lenders share information. Brown, Jappelli, and Pagano (2009) use firm-level data in the transition countries of Eastern Europe and former Soviet Union to show that information sharing is associated with improved availability and lower cost of credit to firms, especially for more opaque firms and for firms in countries with weak legal environments.</mark> Most studies distinguish between private and public credit registries, which have different policy prescriptions for governments wanting to reduce information asymmetry problems in access to finance for firms. Credit bureaus may operate on the principle of reciprocity collecting and distributing the information supplied by its members (private credit bureaus) or may be managed by country central banks that mandate the reporting of data on borrowers (public credit registries). Jappelli and Pagano (2002) <mark>find that public intervention is more likely where private arrangements have not arisen spontaneously and creditor rights are poorly protected. Other studies point to the differing impact of private versus public credit registries. Love and Mylenko (2003) find that existence of private credit registries is associated with lower financing constraints and higher share of bank financing, while the existence of public credit registries is not. However, Djankov, McLiesh, and Shleifer (2007) find that public registries are associated with more private credit but only in poorer countries while private credit bureaus are associated with more
30 euros to 50 euros by mid-May 2020. Using this shock, I compare matched merchants over the period before and after the contactless payment limit increase using a similar difference-difference approach. The period before the payment limit increase will allow me to control for the effect that can be attributed to the COVID-19 crisis. It therefore serves as a reference period for the treatment group as well as for the control group. In addition, to better estimate the substitution effect that can be exclusively attributed to the increase in contactless payment limit, I keep only the sales made by loyal customers, i.e. I subtract the sales of new customers from the total sales. My analysis is based on the proposals put forward in section 2.2, in particular the proposition 2. Table 7 presents my main findings. I find evidence, after controlling for the effects of the COVID-19 crisis and eliminating new sales, that the increase in the contactless payment limit accelerates substitution between card sales and sales from other payment methods for merchants with contactless payment technology compared to those without it. Substitution is estimated by an average monthly increase of 3.5 percent in card sales amount from non-card sales amount (Column (1)). The coefficient is economically and statistically significant at 1 percent level. Substitution between card and non-card payments after the contactless limit increase is higher for both large merchants and new entrepreneurs. The actual effect is not attributed to the crisis nor to the attraction of new customers. This result is consistent with the European Central Bank survey which shows that card payments increase while cash payments decrease during the pandemic (European Central Bank, 2020b). Ditto for Jonker et al. (2020), who show that COVID-19 and the accompanying measures taken by the government and banks have modified consumer payment behavior and accelerated the shift in payment preferences towards contactless card payments.

28
The household survey data used for analysis (EHCVM 2020) contains information on household aggregated consumption expenditures in nominal terms and the monetary value of household assets. It provides extensive household and individual welfare indicators used in estimating objective and subjective poverty measurements and labor market outcomes. The aim of this paper is to empirically estimate the impact of the Covid-19 pandemic on household expenditure, asset value and ownership, poverty status, and labor market outcomes in the Comoros. To achieve the above, the paper analyzes the impact of the virus curtailment measures at both the household and individual levels. The household-level analysis explores total per capita household expenditure, asset accumulation, and poverty. We construct the log of household per capita consumption expenditures from the estimated consumption expenditure for a given household. We extend our analysis by constructing monetary and non-monetary outcome measures for household asset accumulation. The monetary measure captures the log value of total assets owned by the household. The non-monetary household welfare metric includes the total count of assets owned by a household, the different types of assets, and the total count of livestock ownership.<sup>7</sup> Our last household measure considers poverty status using both objective and subjective measures. The objective poverty status is a binary variable that takes the value 1 if a household is below the national poverty line and 0 otherwise.<sup>8</sup> The subjective poverty measures are three separate binary variables taking the value 1 if a household self-reports as “living averagely well”, “living in difficulty”, or “living rich” according to their socio-economic standards, respectively, and 0 otherwise. The binary subjective measures come from a categorical subjective measure of poverty. The motivation for creating binary subjective

> 7 Assets include chair, table, bed, mattress, cupboard, carpet, iron, stove, gas cylinder, oven, food processor, fruit press, refrigerator, freezer, fan, radio, TV, DVD, Satellite dish, washing machine, dryer, vacuum cleaner, air conditioner,
_DESMIGRATIF_ program in areas with higher migrant concentrations, which tend to be more rural.<sup>3</sup> The sample includes 13,318 households, with an average of 43 households per village.

The survey is conducted in two parts. A first questionnaire is administered to the most knowledgeable individual regarding household affairs. This questionnaire covers a roster of household members with basic demographic, education, and employment participation questions; household agricultural and non-agricultural enterprises, assets (including home conditions, e.g. material used for walls of the residence), access to savings and loans, receipts of social assistance programs, economic shocks; and proxy questions about current and return migrants’ migration experiences (henceforth, referred to as the “household survey”). The second part of this survey randomly selects one eligible individual aged between 18 to 40 in each household to be interviewed in more details (henceforth, referred to as the “individual survey”).<sup>4</sup> It complements the education and employment information from the household survey, with additional questions covering literacy skills (in national and foreign languages), job search, and hours of work. It also includes detailed questions on interest to migrate, intended occupation and destination country, personal migration experience, social network pertaining to other migrants, interactions with migrant brokers, expectations of salaries, costs, and work conditions in destination countries, and perceptions of risks and returns from migration. We use most of the questions in the household and individual surveys combined, and we either include the questions as-is, recode them as dummy indicators, or define new variables from multiple questions.<sup>5</sup> We take a broad view of what can be a potential determinant for migration to explore other potential determinants that have not been established in prior literature or that are not commonly collected in other surveys, such as individual perceptions of success and means to achieve success. We use virtually all variables collected in the survey.<
>international mobility|Lack of consistent<br>information<br>(across countries,<br>within countries,<br>over time)|Collection of migration histories<br>and more systematic<br>retrospective/recall data in panel<br>waves for in-between years;<br>associate module; potential links<br>with complementary labor<br>mobility data|
|Liquidity<br>constraints,<br>mobility_vs_<br>immobility|Disentangling the<br>relationship<br>between<br>migration, wealth<br>and resources|Dearth of data on potential<br>migrants, intention-to-<br>migrate, stated_vs_revealed<br>preferences, reasons for<br>migrating|Small migrant<br>samples; indirect<br>proxies/measures<br>for migration; no<br>information on<br>potential migrants|ITM module; empirical focus on<br>immobility traps|
|Migration as<br>adaptation|Understanding the<br>linkage between<br>agricultural<br>practices,_in situ_<br>adaptation and<br>migration;<br>voluntary_vs_<br>involuntary<br>migration|Data gaps on a set of<br>indicators capturing the<br>interconnectedness between<br>farmers’ adaptation and<br>decisions to migrate|Lack of an<br>integrated and<br>comprehensive<br>framework on<br>adaptation|Adaptation Module (in which<br>migration appears as a strategy<br>among a pool of other adaptation<br>options)|

36
24.9|
|formal|34|14.4|16.0|6.7|48.4|
|**Mediumperformance**||||||
|informal|157|12.0|12.7|1.0|5.9|
|formal|104|12.0|13.1|1.7|15.3|
|**Lowperformance**||||||
|informal|176|9.8|10.6|0.5|1.0|
|formal|25|10.3|11.7|4.4|3.2|

Note: Unweighted data. The clusters were created through the k-medians command of Stata.

Which firms are in the high-performance cluster, how different are they from firms in the medium- and low-performance clusters, and what are the main and missing “good” characteristics of informal firms in the high-performance cluster, relative to formal firms? Table 14 provides information about general features as well as the extent of heterogeneity across clusters for the “good” characteristics that are statistically significantly associated with performance outcomes, for both informal and formal firms. An important finding—aligned with the previous findings based on the substantial share of informal firms in both total firms having good characteristics and in the top productivity and sales deciles having good characteristics—is that one-third of all firms (16 out of 50) in the high-performance cluster are informal firms. In terms of the total number of informal firms, this represents 5% of informal firms, with the remaining 45% and 50% of informal firms being in the medium- and low-performance clusters, respectively.

> 307 firms, 194 of which are informal. The lowest productivity group consists of 153 firms with only 16 formal firms. Regarding the presence of informal firms in clusters of relatively high-performance firms, the two approaches yield the same qualitative result, namely that in both cases clusters of “high
country.<sup>34</sup> Since the expansion of private schooling has been much larger in India compared to that in China during the study period, the value of _θ_ 2 is likely to be higher in India.

It is important to recognize that the “returns to education” for the parents (i.e., _R_<sup>_pj_</sup> ) differ from most of the available estimates of returns to education for three reasons. First, we are interested in the total income of all household members rather than the individual income (i.e., not only father’s income). Second, the focus of the existing literature has been on labor market returns, while our analysis requires both labor and non-labor income. Third, father’s education in our analysis is not only a measure of human capital, but a summary statistic for a family’s socio-economic status and captures the effects of other correlated factors, for example, mother’s education due to assortative matching in the marriage market.<sup>35</sup> Thus, we need a measure of permanent household income.

The Chinese Household Income Project (CHIP) provides us with high quality household income data for rural households for multiple years (5 years in CHIP 2002, and 3 years in CHIP 1995).<sup>36</sup> Unlike China, the data on household income in rural India are, however, more limited; we are not aware of any household survey data set that has good quality income information for consecutive multiple years, similar to the CHIP data on China. We thus take household expenditure reported in the National Sample Survey as our measure of household permanent income.

Table 4 (panel A) provides estimates of household-level returns to education, _R_<sup>_pn_</sup> and _R_<sup>_pf_</sup> , in rural China and tests the null hypothesis that _R_<sup>_pn_</sup>
are disaggregated at the first administrative level and data are disaggregated at the second administrative level. <mark>Each of the five elements assessed in the ODIN</mark> data accessibility/ openness dimension are assessed as follows: machine readability; nonproprietary; download options; metadata available and terms of use. All th <mark>e elements in the ODIN coverage and openness dimensions are assessed across several data categories and data dimensions (</mark> see annex table A3 and A4 for the full list of data categories, representative indicators in each category and scoring option for the 2018 ODIN). The default ODIN overall score gives equal weights to the three dimensions <mark>. ODIN scores are calculated as a percentage of the maximum score obtainable.</mark>

> 20 Open Data Watch - Open Data Inventory http://www.opendatawatch.com

> 21 World Wide Web Foundation- https://opendatabarometer.org/barometer/

> 22 Global Open Data Index - https://index.okfn.org/

> 23 The Statistical Access Indicator (SAI) by Almeida and Hoogeveen (2015) with 72% (36 out of 50) of its elements assessing data access and covering 49 African countries would have made the fifth indicator. However, it is unpublished, hence excluded from the review.

> 24 The Statistical Access Indicator (SAI) by Almeida and Hoogeveen (2015) with 72% (36 out of 50) of its elements assessing data access and covering 49 African countries would have made the fifth indicator. However, it is unpublished, hence excluded from the review.

16
**Table 4. Robustness check: First-stage fixed effects panel regression**

||I|II<br>|III<br>Aggregate EW|IV<br>AP<br>All EMDEs|V<br>Baseline with|VI<br>Women|VII<br>'s EWAP|
|---|---|---|---|---|---|---|---|
|Dependent variable: Change in<br>EWAP|Baseline|All countries,<br>1960-2019|All EMDEs|with trimmed<br>sample|10-year<br>averages|Baseline (all<br>women)|Women in non-<br>agriculture|
|Labor productivity growth|-0.1192***|-0.0527***|-0.1069***|-0.1671***|-0.0503***|-0.1043***|-0.0387***|
||[0.0218]|[0.0114]|[0.0211]|[0.0151]|[0.0191]|[0.0192]|[0.0113]|
|Working-age population<br>|-0.1947***|-0.2726***|-0.1882***|-0.2365***|-0.0896***|-0.1879***|-0.1038***|
|growth|[0.0368]|[0.0800]|[0.0353]|[0.0302]|[0.0331]|[0.0355]|[0.0342]|
|Lagged EWAP|-0.0622***|-0.0437***|
Figure 3.6 illustrates the importance of investment quality for sub-investment grade countries and the lack of such an effect for investment grade countries. A comparisons of panels 2B of Figure 3.6 with panel 3 of Figure 3.5 shows that the estimates for the non-investment grade subsample are more precisely estimated and have a steeper slope, implying a stronger effect of investment quality in the sub-investment grade subsample than in the full sample of Table 3.3.

# _Robustness checks._

We check whether our results are robust to using alternative estimation frameworks and measures of investment quality.

As a first step, we re-estimate our baseline model using the Mundlak-Chamberlain correlated random effects. This model yields estimates consistent with the fixed effects model while allowing for time-invariant covariates, which, inter alia, enables us to examine the direct impact of the PIQF index. Columns 1 and 2 of Annex Table A3 and associated marginal effects in Figure A3 show that PIQ-F has a negative and statistically significant impact on sovereign risk, although the nonlinear effect is not as strong in the case of sovereign ratings.

Next, we show that our baseline results are robust to dropping outliers (columns 3-4 of Table A3 and associated panels of Figure A3). Outliers here are defined as observations with values exceeding two standard deviations from the mean for public investment-to-GDP ratio, the PIQ index, and the sovereign risk measure used in the model.

We also show that the results are robust to using the Emerging Markets Bond Index (EMBI) spread as an alternative measure of sovereign risk (columns 5-6 of Table A3), as well as to using ordered logit and ordered probit with year fixed effects as an alternative estimator for the sovereign rating variable (columns 7-8 of Table A3).

Finally, we re-estimate our baseline models using the index computed over a 5-year estimation window (PIQ-Q). One advantage of the PIQ-Q index is that it allows us to use a panel with nonoverlapping periods (we can only do this for sovereign rating because the spreads data do not have sufficient
offers a useful guide on the use of dynamic panel estimators in these situations.<sup>14</sup>

> 12 These are primarily fuel, metals, and ores, whose total export values for the years 1982-2006 I obtained from partner country records in COMTRADE. Because the values are inclusive of cost, insurance, and freight (c.i.f.), I use an ad hoc coversion factor of 1.1 to bring them closer to their f.o.b. values.

> 13 See Bolt et al. (2002).

> 14 Roodman (2009) states that dynamic panel estimators are suitable in the following situations: (i) panels that have a relatively small number of years but large number of countries; (ii) the depen-
study conducted by Fazzari, Hubbard, and Peterson (1988), a large number of investigations on investment have highlighted that smaller firms are more likely to face liquidity constraints.

These findings, in trmn, imply that monetary policy shifts have an important distributional aspect that cannot be tackled within the traditional money view.<sup>20</sup> Furthermore, since smaller and faster growing firms are likely to be firms with highly profitable investment opportunities, the fact that they bear a disproportionate share of the burden imposed by a recession has serious consequences for social welfare: not only are recessions associated with both aggregate output and investment declines, but the declines themselves are inefficient.

While the largest industrialized countries have been the subject of the above mentioned studies, very little wvork has been conducted to investigate the validity of distributional consequences of monetarv policy shifts for developing countries.<sup>2 1</sup> This section attempts to fill an important void by investigating whether monetary policy shifts have a greater influence on the production of sinall and medium sized manufacturing firms than on that of larger firms in one such developing country, Malaysia.

The first step of the empirical investigation is to construct a reasonable proxy for the production of small and medium sized as well as large manufacturing firms. To this end, the paper relies on the information published by the Federeation of Malaysian Manufacturers (FMM) on small and medium sized industries (SMIs) and on industrial surveys conducted by the Department of Statistics. Appendix 1 presents both information on SMIs in Malaysia as well as the compostion of manufacturing component of the industrial production. Available information suggests that the majority of SMIs operate in: (i) food and beverages; (ii) fabricated metal products; (iii) wood and wood products; and (iv) basic metal. Using the corresponding weights 20 See Cecchetti (1995) for more on this.

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**2 Appendix E: Endline Results**

Table (E1) Impacts of the Program on Primary Outcomes

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Overall Housing Quality (Z-Score)|0.24|(0.23)|[0.99]|0.00|(1.00)|1,422|
|_Total_Monthly Housing Expenditures (USD PPP)|-84.71***|(33.10)|[0.06]|227.72|(208.45)|1,421|
|Total Household Consumption (Log USD PPP)|-0.03|(0.08)|[1.00]|8.73|(0.55)|1,422|
|CESD Score (Higher: Less Depression)|-0.07|(0.16)|[1.00]|0.00|(1.00)|1,396|
|SDQ Score (Higher: Better Child Wellbelling)|-0.16|(0.18)|[0.99]|0.00|(1.00)|905|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2021 in-person data. Each row is its own dependent variable.

- Overall Housing Quality is defined as a normalized housing quality index that includes indicators for quality floors, roofs, and walls, indicators for access to grid electricity and piped water, and the number
As yet we cannot predict the future with any degree of scientific validity; we evaluate the current of intergenerational transfers from a recent comprehensive survey.

With detailed and high-quality data on intergenerational transfers as well as rich information on both parents and their children, the China Health and Retirement Longitudinal Study (CHARLS) 2008 pilot provides a fine opportunity to achieve this goal. This paper developed empirical models to explore the patterns and correlates of intergenerational transfers between the elderly parents and adult children in Zhejiang and Gansu Provinces.

Contrary to the situations in most developed countries, we find that transfers are predominantly from children to elderly parents, and still play an important role in the support of China’s elderly. Our results reveal that older people with a larger number of offspring more likely to receive transfers, a result indicating the potential challenges faced with dwindling numbers of children. Parental income has a mixed prediction depending on the magnitude of pre-transfer income of each parent. For those among the bottom income group the relationship is positive, but becomes negative for the top income group. Within the family, there is evidence consistent with division of responsibility among children, potentially based on children’s capabilities. For example, highly educated children transfer more frequently, so do married children. Although there is no significant difference in amount of transfers, oldest sons appear less likely to provide transfers, which seems to be in contradiction with conventional wisdom.

20