rafmacalaba/data-use-mentions-extended huggingface dataset:
self-employment are compensated by rising shares for the labor income of employers and, most importantly, for social security incomes. The relative mean for social security transfers doubles from 10% in 1981 to 20% in 2004, reflecting both the ageing of the population and the expansion and growing generosity of Brazil’s social security system (which is therefore likely to be unsustainable). Unfortunately, this expansion has taken place in a regressive manner, with the correlation between social security incomes and total household incomes rising from 0.36 in 1981 to 0.44 in 2004.

Social assistance transfers – including programs such as the old _Bolsa Escola_ and the new _Bolsa Família_ – are not included with social security incomes. Instead, they are lumped together with

> 26 This is as one might expect from the heterogeneous composition of this residual income category, which includes potentially large rental and capital incomes accruing to rich respondents, as well as small cash transfers to very poor households. With recent improvements in the disaggregation of the PNAD questionnaire, it would already have been possible – albeit still with some assumptions – to separate these disparate income sources for 2004. But this is not possible for earlier years.
||||||||
||**North**|239417|1443|234|509|604|240626|240351|240257|0.5|0.4|0.4|
||**Center**|231262|1486|19|40|47|232729|232708|232701|0.6|0.6|0.6|
||**South and**||||||||||||
||**Islands**|141700|2154|6|17|21|143848|143837|143833|1.5|1.5|1.5|
||**Total **|206422|1680|117|255|303|207985|207847|207799|0.8|0.7|0.7|

Source: Our elaboration on SHIW data.

(*) The reference person is defined as the member with the highest income.

**Table A5 – Differences between adjusted and unadjusted* net wealth shares held by tenth of households, Italy, 1991-2016** _(percentages)_

|**Tenth of**<br>**households**|**1991**|**2004**|**2006**|**2008**|**2010**|**2012**|**2014**|**2016**|**Average**|
|---|---|---|---|---|---|---|---|---|---|
|**1**|0.02|0.01|0.06|-0.04|0.02|-0.03|-0.04|0.05|0.01|
|
**Public Credit Registries as a Tool for Bank Regulation and Supervision** Matías Gutiérrez Girault & Jane Hwang<sup>1</sup>

> 1 Banco Central de la República Argentina and World Bank, respectively. We wish to thank the insightful comments and suggestions received from Ramón Santillán and Jesús Saurina (Banco de España), Christian Castro (Bank of England), Jaime Coronado, Javier Guzmán, and Raúl Morales (CEMLA), Massimo Cirasino, Lily Chu and Cornelio Pimentel (World Bank). Any remaining error is our sole responsibility.
of food or quantities consumed per day (or meal), nor does it account for intra-household distribution of food. With these limitations in mind, we adopt the FCS as a measure of food consumption and later show that it proves itself to be a sufficient metric to unveil the effect of the quality of the rainy season on food consumption.

In Mauritania, Food Security Monitoring Surveys (FSMS) are conducted twice a year and provide first-hand information on the food situation for intervention planning purposes, including FCS-based food insecurity rates at sub-national levels. We focus mainly on the first round that is carried out during lean seasons when food consumption is usually at the lowest in the annual cycle.<sup>5</sup> In our study case, we use a pooled sample of 10,969 household observations from five FSMS rounds collected during lean seasons between 2011 and 2015.<sup>6</sup> Each household observation has consumption frequencies in the food groups required for the calculation of the FCS as well as key household characteristics. Over the five lean seasons, we observe large variations in the moderate food insecurity rates in rural areas, from 29% in 2013 to 42% in 2012 (see Figure 1). Note that the worst two years (2012 ans 2015) followed the poorest rainy seasons (2011 and 2014) observed in the period in terms of aggregate rainfall.<sup>7</sup> Moreover, our data clearly reveal seasonal patterns between lean and post-harvest seasons with an average food insecurity rate of 34% for the former against 22% for the latter.

> 5Another round is conducted during the post-harvest period (December-January).

6There was no survey conducted in 2016 and, from 2017 onwards, data were collected late in the rainy season which poses comparability issues with the interaction of the preceding and ongoing rainy season quality in food consumption outcomes.

7We calculate a rural population-weighted average of department-level 5-month precipitation z-scores and find a value of -0.7 and -0.3
/mark>_ <mark>, 14(2): 2-31.</mark>

- <mark>Brubaker, J. M.; Kilic, T.; Wollburg, P. R..2021. “Representativeness of Individual-Level Data in COVID-19 Phone Surveys: Findings from Sub-Saharan Africa.” Washington, D.C. : World Bank Group.</mark>

- <mark>Bundervoet, Tom, Maria E. Dávalos, and Natalia Garcia. 2022."The Short-Term Impacts of COVID-19 on Households in Developing Countries." World Development, forthcoming.</mark>

- <mark>Castaneda Aguilar, R. Andres, Reno Dewina, Carolina Diaz-Bonilla, Ifeanyi N. Edochie, Tony Fujs, Dean Jolliffe, Jonathan Lain, et al. 2022. “April 2022 Update to the Poverty and Inequality Platform (PIP): What’s New.” Global Poverty Monitoring Technical Note Series 20, World Bank, Washington, DC.</mark>

- <mark>Chancel L., Piketty, T., Saez, E., Zucman, G. et al. 2022. “World Inequality Report 2022.” World Inequality Lab wir2022.wid.world. https://wir2022.wid.world/wwwsite/uploads/2022/02/WIR_2022_FullReport.pdf.</mark>

- <mark>Clark, Andrew E., Conchita d’Ambrosio, and Anthony Lepinteur. "The Fall in Income Inequality During COVID-19 in Four European Countries."</mark> _<mark>The Journal of Economic Inequality</mark>_ <mark>19, no. 3 (2021): 489-507.</mark>

- <mark>Deaton, Angus. 2005. "Measuring Poverty in a growing world (or measuring growth in a poor world)."</mark> _<mark>Review of Economics and Statistics</mark>_ <mark>87 (1): 1-19
externality inherent to green innovation, it may be that demand-side policies are also needed to create sufficient incentives to make the leap to greener products.

**Figure 7: Export of Green + Close-to-Green Goods and Services** (as % of all exports)

<!-- Start of picture text --> 20%<br>16%<br>12%<br>8%<br>4% 2000<br>0%<br>2005<br>2010<br><!-- End of picture text --> Source: COMTRADE and Proximity Matrix based on 6-digit COMTRADE

# **2.5 Little BoP green innovation to-date**

BoP (Base-of-Pyramid) innovations are defined as innovations to meet the needs of poor consumers. They include formal innovations for the poor, namely innovations by global and local private companies and public institutions, whether fully privately provided, supported by public subsidies, or produced through public-private partnerships (such as medicines for neglected diseases and seeds for ̳neglected‘ soil types and climates). They also include informal innovations by local grassroots inventors largely through improvisation and experimentation.<sup>14</sup> Often facilitated by co-creation with poor consumers themselves, the innovations typically seek to better meet the needs of poor households at dramatically lower costs per unit, aided by significant scale-up in volumes – hence they seek ―to create more (products) with less (resources) for more (people)‖.<sup>15</sup> An exhaustive survey of green BoP innovations indicates that very few BoP (and related low-tech) green innovations have been sufficiently scaled-up to-date. Whether there may be a need for more focused policy efforts in this area requires a better understanding of the constraints, both on the supply and demand side, impeding scaled-up commercialization, and the benefit-cost of appropriate policies and their implementation to improve market outcomes. Box 1 highlights a few representative green BoP innovations that have been documented.

## **<
if local or provincial/state government grants<br>the contract,0 otherwise|PPI database and authors’<br>collection|
|Physical Investment|Total investment in physical assets, in 2020 billion<br>US dollars|PPI database and authors’<br>collection|

**Table B.2 Definition of macroeconomic variables**

|_Variable_|_Definition_|
|---|---|
|Annual GDP per capita growth rate|Annual GDP per capita growth rate for previous year, detrended and<br>demeaned using the filter suggested by Hamilton (2018). Calculated using<br>the GDP per capita growth series from_World Development Indicators_<br>(WDI).|
|Annual depreciation|Depreciation rate of local currency against US dollar the previous year,<br>detrended and demeaned using the filter suggested by Hamilton (2018).<br>Calculated using the “DEC alternative conversion factor (LCU per US$)”<br>series from the WDI.|
|Banking crisis occurred|1 if a systematic banking crisis occurred in the country the previous year, 0<br>otherwise(from Laeven and Valencia 2018)|
|Debt crisis occurred|1 if a debt crisis occurred in the country the previous year, 0 otherwise<br>(from Laeven and Valencia 2018)|
|Exchange rate crisis occurred|1 if an exchange rate crisis occurred in the country the previous year, 0<br>otherwise(from Laeven and Valencia 2018)|

37
**Omitted variables.** We control for a wide range of time-varying industry-specific agglomeration factors. We know since Marshall (1890) that industries may concentrate for various reasons that are unrelated to the transport costs of goods, but linked to the costs of transporting people and ideas (see Duranton and Puga, 2004, for a review). Knowledge spillovers and labor market pooling are among the most important ‘Marshallian’ factors. Industries may also concentrate geographically because of localized natural advantages like natural resources or raw materials. The importance of controling for these has been pointed out, among others, by Kim (1995), Ellison and Glaeser (1999), and Ellison _et al_ . (2010). Furthermore, various other characteristics linked to the industrial organization of industries – industry size, the mean plant size, the size distribution of in the the of multi-unit or owner- plants industry, presence firms, foreign ship – are also likely to affect their spatial structure (see, e.g., Rosenthal and Strange, 2003). We account for these time-varying factors as follows. First, we control for knowledge spillovers using as a proxy an industry’s research and development (R&D) intensity, i.e., the ratio of R&D expenditure to total output of that industry (see Rosenthal and Strange, 2001). Second, we employ a proxy related to workers’ educational attainment to control for aspects of labor market pooling. More specifically, we use the share of hours worked by all workers with postsecondary education in the total number of hours worked in the industry.<sup>11</sup> Third, we control for the importance of natural advantages using the share of inputs from natural resource-based industries, and the sectoral energy inputs as a share of total sector output. Last, we control for basic industry structure and scale effects by including the following controls: total industry employment; mean plant size; the Herfindahl index of firm-level concentration; the share
28 percent in 2004-2005. The corresponding decline in urban India is from 42 percent in 1983 to 26 percent in 2004=2005. See Bank (2011).

> 38This evidence of no signicant improvements in educational opportunities in India is in contrast to the evidence of substantial improvements based on the estimates of intergenerational regression coecient (IGRC) in educational attainment reported by Azam and Bhatt (2015), Jalan and Murgai (2008), Kishan (2018), and Maitra and Sharma (2010). However, this conclusion is consistent with the analysis of Emran and Shilpi (2015) which uses two rounds of DHS (called NFHS in India) surveys (1992/93 and 2006) and focuses on the 16-27 year old children in the survey year. Using sibling correlation and intergenerational correlation (IGC), they show that there has been almost no change in educational opportunities from 1992/93 to 2006. Since IGRC and IGC are partial measures and cannot take into account many factors shared by the siblings, one can make a plausible argument in favor of the conclusions based on sibling correlation estimates. We will discuss later the changes in the share of intergenerational correlation (IGC) over time in our sibling correlation estimates. Please see section 6 below.

21
pre-RSE wage earning data (although only a minority of households earned wage income). Furthermore we know whether households tried to participate in the RSE (by having a member register for the work-ready pool, or apply directly to an employer). Finally, we also have a plausible reason why some households participated in the RSE and other households with these same characteristics did not – there was excess demand for RSE employment, and so not all households who wanted to participate were able to.

We estimate two versions of the propensity score, which we denote PS-1 and PS-2. They differ only in that PS-2 first restricts the sample to households which applied to participate in the RSE before estimating the score, eliminating the non-applicant households. This allows us to explicitly screen on demand for the RSE, although given that the reason many non-applicant households said they didn’t apply was lack of information about the program (Gibson et al, 2008; McKenzie et al, 2008), failure to apply need not imply lack of demand.

We then use six main categories of variables which we believe may influence participation in the RSE to estimate the propensity score: demographic variables (household size, number of males aged 18 to 50, number of adults, number of school age children); characteristics of the 18-50 year old males in the household, who are the individuals most likely to participate (share literate in English, share with schooling beyond grade 10, the share with self-reported health rated as very good, the share who drank alcohol in the past month, and the mean number of days of hard labor carried out in the past month); the household’s previous experience and network in New Zealand (share of adults who had previously been to New Zealand, number of relatives in New Zealand); household baseline assets and housing infrastructure (an asset index comprising the first principal component of durable goods owned, the number of pigs, cattle and chickens owned, and whether the dwelling was traditional style); geography (on
# **3.3. Measuring the Quality of Local Governance and Decentralization**

The quality of local institutions and local governance is measured through various proxies. First, we constructed an objective measure of corruption based on the incidence of bribe payment in three steps. In the first step, we derived a binary indicator from answers to the question _”How often, (if ever, have you had to pay a bribe, give a gift, or do a favour to government officials in order to:...”_ , to which the respondents indicate whether they have bribe government officials in order to obtain public services such as school placement, medical services, official documents, household services and police help. In the second step, the binary indicators are then aggregated using polychoric correlation to create an indicator of the tendency of bribe payment at the individual level. In the third step, the individual estimates are averaged at the local community level. Unlike the individual perception of corruption, the incidence of bribe payment points to an objective exchange between authorities and residents. Second, we measure the quality of local governance by averaging the individual distrust in local government councillors at the regional level. Lastly, we corroborate the results with a corruption perception indicator, which also takes the regional average number of residents that perceive their local governments as being corrupt.

As per our third contribution to the literature, we introduce a measure of decentralization based on the legal assignment of tax-related decisions to local governments issued from a new dataset on tax and revenue assignment in developing and emerging economies.<sup>1</sup> The dataset provides comprehensive information on the vertical decision-structure over the tax system across government tiers. It was constructed through in-depth reviews of legal and policy documents, scientific and grey literature, as well as fiscal archives from the International Bureau of Fiscal Documentation (IBRD, Access: 2015-2017), which inform on the discretionary power of central and local governments over deciding on tax-related matters. Based on the legal and
Chod et al. (2019) offers a theory of trade credit and supporting empirical evidence that starts from the proposition that, in the presence of multiple suppliers, the benefit of providing trade credit is not fully internalized by a trade creditor. After obtaining trade credit, a retailer can use freed-up liquidity to buy more goods from other suppliers as well as the one who provided the credit. This leads to a free rider problem because a given supplier bears the full cost of providing trade credit, but the benefits of larger spending by the retailer are shared among all suppliers. Thus, Chod et al. (2019) hypothesize that a supplier that is responsible for a larger share of the retailer’s purchases internalizes a larger part of the benefit and is therefore willing to offer a larger proportion of its goods on credit. This is similar to the equity stake idea from Petersen and Rajan (1997) except that it emphasizes the importance of the supplier’s position among firms selling to the _same customer_ rather than its competitive position (market power) among all firms in its industry.

Using annual data from Compustat, complemented with the Compustat Industry Segment data set, which identifies firms’ principal customers and is used to identify customer-supplier relationships (following the method in Cohen and Frazzini, 2008), for a sample of U.S. retailers from 1996 to 1999, Chod et al. (2019) find a significant positive relationship between (a) the amount of trade credit extended by a supplier and its average share of retailers’ purchases and (b) the amount of trade credit that a retailer receives and the concentration in its suppliers, as reflected in the Herfindahl index of supplier shares of the retailer’s purchases. These findings provide support for a modified version of the Petersen/Rajan equity stake hypothesis, which highlights the importance of a buyer’s retail platform for a supplier in relation to that of other competing suppliers.

Suppliers may compete with each other not only for space on a buyer’
the average marginal effect estimates of credit constraints and green management on the type of green investment, based on multinomial logistic regression with the type of green investment as a dependent variable. Regression controls for locality-level credit market controls (log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius) ; population size class; and region and sector fixed effects. In Panel B, estimated coefficients for all firm-level covariates as well as log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius are allowed to vary by region. Locality-clustered standard errors in parentheses. ***, **, * - statistically significant at 1%, 5%, and 10%, respectively.

Source: Enterprise Surveys and authors’ calculations.

The magnitude of the effect of credit constraints and green management quality on the type of green investment varies by region. Estimates in Panel B indicate that while credit constraints matter for some 21
Figure 2a. Oaxaca-Blinder decomposition for productivity gender gap <!-- Start of picture text --> .4<br>.2<br>0<br>-.2<br>-.4<br>Fin Fla HHh Inp Lan Mec NFl NFn SID Sub XIN Total<br>Structural Endowment Combined<br><!-- End of picture text --> Figure 5b. Oaxaca-Blinder decomposition for earning gender gap <!-- Start of picture text --> .4<br>.2<br>0<br>-.2<br>-.4<br>Fin Fla HHh Inp Lan Mec NFl NFn SID Sub XIN Total<br>Structural Endowment Combined<br><!-- End of picture text --> Source: Authors’ estimations using HIES 2016. _<mark>Note:</mark>_ <mark>F</mark> in = access to finance; Fla = number of unpaid agricultural family workers; HHh = household head; Inp = agricultural inputs; Lan = log of cultivated area; Mec = mechanization; NFI = share of nonfarm labor income; NFN = share of nonlabor income; SID = Simpson Index of Diversification; Sub = <mark>subsidy (sum of the coefficients for the incidence of subsidy and the log of the value of subsidy); X</mark> IN = Export Orientation Index.

22
CPIA scores.<sup>29</sup> We also considered the possible relevance of political economy explanations for World Bank decision making. Specifically, we tested for the impact of temporary UN Security Council membership on graduation. Dreher, Sturm and Vreeland (2009) find that temporary members of the UNSC receive more World Bank projects, although loan size is unaffected. One interpretation of this finding is that large donor countries represented on the Bank’s Executive Board may sometimes approve loans in exchange for favorable votes in the UNSC. However, we believe this argument has little relevance to graduation decisions, which have been largely consensual in practice, although the Executive Directors have the authority to graduate a country involuntarily (Shihata, 2000). Consistent with this view, a dummy for temporary UNSC membership falls far short of significance when added to our graduation regressions.<sup>30</sup>

> 28 Calculating coefficient estimates and standard errors for _Credit Rating_ conditional on different years (i.e. the time trend variable), we find negative but insignificant point estimates for 1982 through 1989; with positive estimates thereafter, becoming significant at the .10 level in 1996 and at the .05 level in 1998.  Results on _Credit Rating_ shown in the table are conditional on the average value (1997) of the time trend variable.

> 29 CPIA ratings for individual IBRD countries cannot be made public under Bank policy, which is why only aggregates are reported here.

> 30 Results available on request.  Similarly, voting compatibility with the interest of major donors in the UN General Assembly does not help explain graduation decisions.  Kilby (2006), among others, finds evidence that the geopolitical interests of large donor countries, as proxied by General Assembly voting patterns, affect aid allocations of some international financial institutions.

17
|19|24|14|28|14|73|73|57|41|
|Poland|13|19|26|16|22|41|66|40|42|30|22|
|Slovak R.|19|30|41|25|35|30|37|46|44|14|12|
|Slovenia|21|21|22|10|14|37|47|35|25|9|12|
|**Bulgaria**|**8**|**5**|**4**|**24**|**24**|**66**|**60**|**49**|**41**|**28**|**64**|
|Croatia|7|5|8|16|14|96|84|37|49|33|61|
|Romania|4|6|10|14|10|83|89|78|19|42|43|
|Albania|0|3|2|41|97|21|73|10|54|47|57|
|Macedonia|3|3|2|2|2|70|86|26|63|7|14|
|Moldova|12|5|3|11|13|16|34|17|36|67|29|
|Serbia & Mont|10|13|11|10|14|80|85|46|34|3|9|

Note: For Serbia and Montenegro the most recent data available are for 2002. Source: Computations based on UN COMTRADE Statistics.

Bulgaria fits neatly into two different patterns of specialization that appear to have been emerging in Central Europe since 2000. First, like most CEEC-10 economies that ‘plugged-in’ automotive networks, it has been moving towards