rafmacalaba/data-use-mentions-extended huggingface dataset:
, higher backward and forward GVC participation is linked with a higher average RTI (Table 1, Columns 2 and 3).

As backward and forward GVC participation measures are standardized within the sample, the larger (in absolute terms) coefficient of backward GVC participation suggests this variable’s relatively greater importance relative to forward GVC participation.<sup>9</sup> The worker-level RTI is standardized with the United States mean and standard deviation, to provide a reference point and comparability with the widely used (Acemoglu & Autor, 2011) RTI measure based on the US O*NET data. The interpretation would be the following. An increase in backward (forward, resp.) GVC participation by one standard deviation is associated with a rise in worker-level RTI by 0.079 (0.053, resp.) the US standard deviations.

The negative interaction terms with GDP per capita for both GVC measures imply that the relationship between GVC participation and RTI weakens with countries’ development levels. We provided the rationale for this finding in the case of forward GVC participation under the overall findings in the previous paragraph. Similarly, higher backward GVC participation in high-income countries captures more high-value added worker tasks – even in the assembly stage – while in lower-income countries, those tasks tend to be more repetitive (think of technician operating machines in the former versus assembly line workers in the latter). For example, in Ecuador (relatively low GDP per capita, about 1 log point below the sample average), one standard deviation higher backward GVC participation among workers performing offshorable occupations is associated with RTI higher by 0.133. In contrast, in Canada (relatively high GDP per capita, about 1 log point above the sample average), it is associated with RTI higher by only 0.025. Considering that backward GVC participation in both countries is

> 9 As a robustness check, we run models for backward and forward GVC participation measures separately, rather than combining them in one joint regression, and obtain similar results
important. Second, since 2014 there have been several policy changes whose impacts can be picked up in the updated analysis, though in some cases this will be confounded by the fact that this paper also introduces some methodological upgrades. For example, Lustig and Yang (2020) treated all income from pensions as government transfers. This paper will depart from this assumption and attempt to treat contributory pensions which have characteristics of deferred income differently than pensions through the resident’s system, which are more transfer-like.

Following standard practices of fiscal incidence analysis, the study combines information from administrative sources on government revenues and spending with household survey data. This allows us to identify how much households pay in taxes and receive in transfers and subsidies, across the income distribution. The analysis includes fiscal elements such as personal income tax, contributions to social insurance, value-added tax, consumption tax, cash transfers, contributory pensions, and spending on education and health and accounts for 63 percent of total revenues and around 43 percent of total government spending. Several potentially important components of taxes and spending are not included in the analysis. These include, for example, corporate income taxes and non-tax elements on the revenue side and spending on infrastructure on the spending side. Both instruments can have sizeable redistributive impacts both in the short and medium run, but there is no satisfactory way of determining the incidence of these taxes on individual households. This practice is standard in fiscal analysis exercises and acknowledged as a caveat of the methodology.

First, consistent with previous studies, this paper finds that fiscal policy achieves a sizeable inequality reduction (10.3 Gini points) in China. However, China places around the median of upper-middle-income country peers on

> 2 Lustig, Nora and Yang Wang 2020. "The Impact of Taxes and Transfers on Income Inequality, Poverty, and the Urban-Rural and Regional Income Gaps in China," Commitment to Equity (CEQ) Working Paper Series 93, Tulane University, Department of Economics.

> 3 The Commitment to Equity approach refers to
which panel data is collected represent the primary data for analysis; they include the sample plot (described above) and the farmer’s next most important plot (defined at baseline; we refer to this as the “most important plot” or “MIP”). We also collected data on household characteristics, labor force behavior, and a short consumption and food security module. In analysis, we will focus on the sample plots to learn about the effects of the irrigation itself, and the most important plot to learn about how the presence of irrigation on the sample plot impacts households’ productive decisions on their other plots.

Three follow up household surveys were conducted in May - June 2017, November

> below in Section 6. Additionally, only two of the three sites have a viable boundary of cultivable land both just inside and just outside the command area; we use only these sites for our analysis of the impacts of access to irrigation in Section 3 and Section 5.

> 17This was to discard forest, swamps, thick bushes, bodies of water, or other terrain which would make cultivation impossible.

11
linked this variable to a 2019–23 VC-level panel of area cultivated with winter and summer crops for the entire territory of Ukraine (including areas beyond Ukrainian control). This allowed us to analyze separately the direct (due to damage to a specific field) and indirect economywide effects of the conflict on area cultivated with agricultural crops. The economywide effects dominated initially, but their relative importance declined, whereas the direct impacts persisted and even increased in size over time.

Data on prices and actual production decisions from public sources and a farm survey in areas that were not directly affected by the conflict showed that although the direct and indirect effects of the war on cropped area are highly significant statistically, conflict-induced changes in transport costs—attributable to the closure of seaborne export routes and damage to physical infrastructure—have economically more important and far-reaching effects on agricultural profitability. Although such large changes in relative prices can provide an opportunity for diversification in a less volatile environment, the data show no evidence of a shift toward higher value crops even among small farmers, suggesting that even in areas not directly affected by the war, such shifts remained limited. Efforts to identify and remove technical, financial, or market-related constraints to farm-level diversification could be an important way to increase farmers’ resilience to the negative impacts of the war.

15
access institutions, such as those securing property rights, as hampering firm growth (for example, de Soto, 1989). Further there tends to be an assumption that formality is an all or nothing state. We argue that these views are valid only as special cases of a more general and continuous relation between the firm and society.

WVe recast the question of formality as the firm's decision of how much to participate in the numerous institutions of civil society: federal and local treasuries, governmental programs such as social security (including pensions and health care), the legal system, the banking system, health inspection, firm censuses, trade organizations, civic organizations, _etc._ We argue that a minimal degree of participation in some institutions is a necessary input to growth for many firms, and that participation increases with the success of the business. That is, formality can be viewed as a normal input to production: q = f(L,K,P), where L, is labor, K is capital, and P is participation in (a number of different) societal institutions, and all three inputs are complementary. The benefits of formality, while often overlooked, are numerous. They include, but are not limited to:<sup>6</sup> _1. Enforceable/impersonal contracts and credible signaling._ All entrepreneurs have access tD social relationships to enforce implicit contracts among their friends and family, who form a small number of their potential customers and employees. Participation in the legal system is needlessly expensive for transactions with these individuals. Similarly, old age and health insurance may be easily handled by insuring through their mutual extended network of friends and family. Property rights secured by personal ties may be sufficient if investment is minimal. These characteristics of small scale economic transactions are commonly observed in developing countries, as well as in many ethnic enclaves in developed countries. But this mode of operation is constrained by the ability of the entrepreneur to maintain personal relations with all involved
= 1, 30.3%|JQM = 1, 5.7%|
|JQM = 2, 36.9%|JQM = 2, 12.8%|
|JQM = 3, 20.3%|JQM = 3, 31.2%|
|JQM = 4, 9.4%|JQM = 4, 49.9%|

_Source:_ Estimates based on data of SEDLAC (Socio-Economic Database for Latin America and the Caribbean), Center for Distributive, Labor, and Social Studies, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, La Plata, Argentina, and Equity Lab, Team for Statistical Development, World Bank, Washington, DC, https://www.cedlas.econo.unlp.edu.ar/wp/estadisticas/sedlac/. _Note:_ The table shows the shares of wage workers at different levels of job quality among formal and informal workers, classified according to the productive definition of informality. A worker is informal if the firm employs five or fewer workers.

Correlation analysis between the single dimensions of the JQM and the two definitions of informality (productive and legal) shows that, overall, the JQM is well correlated with the legal classification of informality (−0.7) and less with the productivity classification (−0.5), given that having access to pension benefits is one of the JQM criteria.<sup>23</sup> At the same time, both informality classifications are only weakly correlated with income and the working conditions dimensions (−0.1). The stability dimension is correlated at −0.47 with legal informality and −0.38 with productive informality. The highest correlation occurs between both informality definitions and the benefits dimension with a correlation of −0.86 for

> 23 The correlation is negative because informality is equal to 1, while the lowest job quality score is equal to 0.

25
Penn World Table. The results are comparable with the methodology used in Patel et al (2021) suggesting that the steep negative slope of the regression line during the Golden Decade has an important impact on the interpretation of convergence, especially in the post-2000 era (Figure 21).

**Figure 21. The selection of time periods to analyze growth impacts the convergence path: most convergence trend of the 2000s and 2010s comes from the Golden Decade. Based on Kremer 2021.**

Source: based on Kremer et al (2021) Note: The horizontal axis shows the natural log of real per capita GDP, and the vertical axis shows average growth of GDP per capita.

**Figure 22 Replicating the Kremer analysis for our three eras approach. The Golden Decade drove convergence but The New Normal suggests a reversal of fortune.**

28
2016.

- **Petersen, Mitchell** , “Estimating Standard Errors in Finance Panel Datasets: Comparing Aproaches,” _Review of Financial Studies_ , 2009, _22_ (1), 435–480.

- **Porta, Rafael, Florencio Lopez de Silanes, and Andrei Shleifer** , “Corporate ownership around the world,” _The journal of finance_ , 1999, _54_ (2), 471–517.

- **Porta, Rafael La, Florencio LopezdeSilanes, Andrei Shleifer, and Robert W. Vishny** , “Law and Finance,” _Journal of Political Economy_ , 1998, _106_ (6), pp. 1113–1155.

- **Puckett, Andy and Xuemin Sterling Yan** , “The interim trading skills of institutional investors,” _The Journal of Finance_ , 2011, _66_ (2), 601–633.

- **Raddatz, Claudio and Sergio L. Schmukler** , “Deconstructing Herding: Evidence from Pension Fund Investment Behavior,” _forthcoming, Journal of Financial Services Reasearch_ , 2012.

- **Rhee, S. Ghon and Jianxin Wang** , “Foreign institutional ownership and stock market –

- liquidity: Evidence from Indonesia,” _Journal of Banking & Finance_ , 2009, _33_ (7), 1312 1324.

- **Sias, Richard W.** , “Institutional Herding,” _Review of Financial Studies_ , 2004, _17_ (1), 165– 206.

24
Exchange Commission, 2018).<sup>16</sup> To get a better idea of the evolution of the Brazilian electrification, the right panel in

> 15 Though public management issues are important, Brazil is not an extreme case of mismanagement according to the public investment efficiency index developed by Dabla-Norris et al. (2012). With an index of 3.12 out of 4, it ranks second within their sample of 71 low- and middle-income nations.

> 16Officers at Eletrobras Termonuclear S.A (“Eletronuclear”), Eletrobras’s owned nuclear power generation subsidiary, engaged in an illicit bid-rigging and bribery scheme involving the construction of a nuclear power plant (“UTN Angra III”) from approximately 2009 until 2015. These officers used their influence at Eletronuclear in favor of a bid-rigging scheme among certain private Brazilian construction companies.

25
# **2. Data**

A new and unique data set on SME financing volumes across the world is collected through the _Financial Access 2010_ survey in January-April of 2010 by CGAP/World Bank Group.<sup>8</sup> The survey annually collects data from the main financial regulators such as central banks or bank supervisory agencies in 140+ countries. One of the objectives of the 2010 survey was to identify the feasibility of collecting consistent cross-country data on SMEs from financial regulators. To achieve this objective, the survey asked about existing institutional arrangements for collecting SME data and about SME definitions used by the authorities. It also requested the most recent statistics on the volume and number of SME loans, as well as the number of SMEs with outstanding loans. The data collected is as of the end of 2009.

As is the case with most data collection endeavors, the survey methodology has some limitations. First, the survey collects information only on regulated financial institutions, leaving out nonregulated providers of financial services. This practice is likely to understate the scale of SME financing, as lending to SMEs is often done by non-regulated credit providers. Second, even though the main financial regulator was asked to provide data on all regulated financial institutions, in cases where some financial institutions are regulated by secondary regulators, the data are rarely available. As a result, the data understate the true scale of overall SME lending.

The survey asks regulators if they monitor the level of lending to SMEs by regulated financial institutions through regular or irregular reporting, through periodic surveys of financial institutions, through collecting estimates from credit registries and/or by using any other method. In addition, in the cases where a different institution than the financial regulator is responsible for monitoring SME lending, regulators were asked to identify this institution.

Out of 74 countries that reported that they collect data on SME finance, nearly 80 percent – 59 countries – stated they collected information on a regular basis. Financial regulators in middle and low-income countries are more
policymakers prepare to accommodate the next wave of rural migrants with China and India for example contemplating the development of super-cities (Henderson, 2010) and Africa also urbanizing rapidly, while finding itself already at high levels of urban primacy (Behrens and Bala, 2013). The lock-in of urbanization patterns, including through infrastructural lock-in, adds further urgency.

Building on Ravallion, Chen and Sangraula (2007) and Christiaensen and Todo (2013), this study further illustrates the differential effects on poverty reduction from metropolitization versus rural diversification and development of secondary towns by presenting stylized facts from a rather exceptional survey documenting nearly two decades of spatial and occupational change for a panel of respondents from Kagera, Tanzania. Cross-country analysis using country fixed effect panel estimation techniques applied to 206 poverty spells from 51 developing countries spread across five continents, spanning 1980-2004, is subsequently applied to examine whether broader empirical regularities can be uncovered.

Practically, in both the Tanzania case and the multi-country study, the population is classified into three groups according to their occupation and location: 1) those living in rural areas and employed in agriculture, 2) those living in mega cities and employed in industry and services, and 3) those living in rural areas and secondary cities and employed outside agriculture. The latter group will be referred to as the “middle”, reflecting its operational definition as the residual category between the total population and those employed in agriculture and those living in mega-cities. Hence the study differs conceptually from most of the literature, which typically applies either a spatial (rural-urban) or an occupational (agriculture-non-agriculture) dichotomy.

In Kagera, Tanzania, 1 out of 2 individuals interviewed in 1991/4 that exited poverty by 2010, did so through transition out of agriculture into the middle, 1 out of 3 while continuing as farmers, and only 1 out of 7 through migration to capitals or other big cities (Dar es Salaam, Kampala, and Mwanza). In addition, while unemployment rates were more than three times higher among migrants in the
have been significant advances in the range of tools available for measuring inequality when there are multiple dimensions, including new classes of measures, axioms for discerning among measures, and dominance methods for ensuring comparisons are robust.<sup>2</sup> In their survey on multidimensional poverty and inequality, Aaberge and Brandolini (2015, p. 201) note a strong demand for multidimensional analyses by policymakers and other stakeholders, and indeed, empirical and policy applications of multidimensional poverty indices

> 1 For Aaberge and Brandolini (2015, p. 146) this sensitivity is “the single feature that distinguishes multidimensional from unidimensional analysis.” Note that it also places additional requirements on the data that can be used. 2 See the recent surveys of Aalberg and Brandolini (2015), Andreoli and Zoli (2020), Glassman (2019), and Seth and Santos (2018).

2
, Gender, and Generations.” _Comparative Education Review_ 64 (4): 725–48. https://doi.org/10.1086/710768.

- Rogers, Halsey F., and Emiliana Vegas. 2009. “No More Cutting Class? Reducing Teacher Absence and Providing Incentives for Performance.” Policy Research Working Paper 4847, World Bank, Washington, DC. https://doi.org/10.1596/1813-9450-4847.

- Rutkowski, Leslie, Eugenio Gonzalez, Marc Joncas, and Matthias von Davier. 2010. “International LargeScale Assessment Data: Issues in Secondary Analysis and Reporting.” _Educational Researcher_ 39 (2): 142–51. https://doi.org/10.3102/0013189X10363170.

37
survey and administrative vaccination figures. When additionally controlling for region fixed effects, the correlation between statistical performance and discrepancies in vaccination rates is no longer statistically significant with the effect absorbed by the Sub-Saharan Africa regional dummy.

We take this as evidence that discrepancies between administrative records and survey data are more likely and larger where administrative data systems are weaker and suffer from lower capacity.

# **Numerator issues (number of people currently vaccinated)**

Next, we turn our attention to the possible sources of bias in the administrative data (Table 10). We distinguish between numerator issues (misreporting of the number of people currently vaccinated) and denominator issues (under- or overestimating target population size).

On the side of potential numerator issues, we find large time gaps in reporting to be common. While the average frequency of reporting is fairly high (every 2.4 days) across our sample of 36 LMICs, gaps can get as long as close to half a year without a vaccination rate report (175 days between July 2022 and January 2023 in Ethiopia). The longest time gaps between two data points are almost two months (54.9 days) when averaging across our sample of countries, with 26 out of 36 countries (72%) having gaps of 30 days or longer. Fourteen countries (42%) have gaps longer than 2 months (60 days). Gaps are even greater in our sample of five Sub-Saharan African countries that are the subject of our survey experiments. Here, the average longest gap amounts to 3 months (90 days) with all countries displaying gaps between reports of 42 days or more.

We also find large jumps in reported coverage between data points in the administrative data. Across our sample of LMICs, as well as in our smaller sample of five Sub-Saharan African countries, we find the average increase in reported COVID-19 vaccine coverage to amount to 0.2 percentage points in between two reports (0.11pp and 0.04pp on a per-day basis, respectively
# **Table 8 Why Don’t People Have Bank Accounts?**

||**Percentage**<br>**among the un-**<br>**banked**|
|---|---|
|**What are the main reasons why you do not have a bank account?**||
|Do not have the amount of moneybanks require to open an account|25.0%|
|Bank fees are too high|16.5|
|Are notquite sure how to open an account|6.7|
|Banks hold checks for too long|2.3|
|Banks are not located conveniently|2.6|
|Banks are not open whenyou need to use them|2.1|
|Most bank staff onlyspeak English|1.5|
|None of the reasons listed above|57.9|
|**Which bank fees are too high?(among those citing bank fees as a barrier)**||
|Monthlyaccount fee|55.4%|
|Bounced check fee|29.8|
|Per check fee|21.8|
|Fee for use of "foreign" ATMs|16.9|
|Annual fee for ATM card|4.7|
|Other fee|24.2|
|Have heard of "basic" checking accounts that charge low fees, set low minimum<br>balancerequirements, and permit you to write alimitednumberof free checks|76.6|
|Have never had a bank account|63.3|

Because of the shortcomings of the OCC survey regarding the reasons that people do not have a bank account, we include the results from one other survey that addressed the same issue. In a 1996 survey of 900 lower-income urban households, Caskey (1997) asked households without deposit accounts, why they do not have an account. He provided respondents with a list of possible reasons from which they could