rafmacalaba/data-use-mentions-extended huggingface dataset:
censored and can therefore be estimated by OLS (or by weighted least squares to control for potential group data heteroscedasticity).

As discussed in Olarreaga, Soloaga and Winters (1999), tariffs tend to be determined at the tariff line level and not at the industry level."<sup>7</sup> This implies that if the error is determined at the tariff line level and the number of tariff lines in each industry is not the same (as can be observed in the table in the data appendix the number of tariff lines varies from 1 in ISIC 3131 to 501 tariff lines in ISIC 3511), then the variance of the error term at the industry level will be negatively correlated with the number of lines in each industry, reducing the efficiency of our estimates. To correct for this potential heteroscedasticity, each observation at the industry level needs to be weighted by the number of tariff lines in each industry (i.e. multiplied by the square root of the number of tariff lines in each industry).

However, a problem with the above correction is that it assumes that the errors at the tariff line level are independently distributed. If observations within the same industry share a common unobserved determinant, then the above correction may reintroduce some heteroscedasticity. To test for this possibility, we follow Dickens (1990) and when necessary, we apply the correction he suggests which yields asymptotically efficient estimates.'<sup>8</sup> The data appendix describes data sources and provides a table with the data used in the estimations. The CET is the one negotiated in Ouro Preto in 1994 by Mercosur members. Tariffs or Argentina and Brazil are 1994 external official tariffs (corresponding to an FTA situation as by then more than 95 percent of their internal trade was free of tariffs). Trade data is the average of 1993, 1994 and 1995 whereas production data correspond to the industrial census of 1985 in Argentina and Brazil

> 1<sup>7</sup> If not we would
overall terms of trade: while the export price index decreases by 0.6 percent, the import price index decreases by 2.3 percent.

> 7 “Nominal” GDP is computed with respect to the Consumer Price Index (CPI). Since the CPI is the appropriate _numéraire_ for the analysis conducted at the household level, nominal GDP is the macro aggregate which changes are consistent with the changes in per capita income at the household level. Also note that poverty and inequality indicators in the microsimulation module are based on total per capita earned income, with no deduction of direct taxes. The issue of tax replacement will be debated in subsequent simulations.

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# **Global Survey of Development Banks**

José de Luna-Martínez and Carlos Leonardo Vicente<sup>1</sup>

**Keywords** : development banks, banking, emerging economies **JEL Classification** : G21, G28, H11, H81 FSE

> 1 José de Luna-Martínez (Jdelunamartinez@worldbank.org) is a Senior Financial Economist at the Financial and Private Sector Development Vicepresidency of the World Bank Group.  Carlos Leonardo Vicente (cvicente@worldbank.org) is a Financial Sector Specialist at the Financial and Private Sector Vicepresidency of the World Bank Group.
tends to happen more often, however, it does not point at corruption definitely. It allows for consistent comparisons across time, sectors, regions, and organizations and can be further expanded and related to using additional corruption proxies. For the ease of interpretation, CRI is calculated on the contract level after each individual risk indicator is standardized to 0: non-risky and 1-risky (sometimes an intermediate risk level is added, 0.5). Hence, CRI falls between 0 and 1, with 1 representing the highest observed corruption risk and 0 the lowest.

We match the unique identifiers of all firms awarded with any type of public procurement contract with the same identifiers in the firm administrative dataset. The combined dataset allows to estimate if GPP contracts are allocated to more or less productive firms. First, we estimate if firms awarded with green public procurement contracts are more productive than firms with a comparable size, age, and ownership structure competing in the same 4-digit sector but not awarded with a state contract. We estimate if the productivity differential changes for GPP contracts carrying a lower risk of corruption. Second, we restrict the sample to firms that obtained a public procurement contract to estimate if GPP contracts are more often allocated to more productive firms than other state contracts. We also estimate the impact of corruption risk in public procurement on the productivity of the awarded firm and the interaction effect between green and corruption-risk contracts.

# 3.4 Identifying the impact of green procurement on procurement and firm outcomes

Identifying the causal impact of green procurement on public procurement and firm outcomes, thus testing our hypotheses, is challenging due to reverse causality, omitted variable bias and sample selection. We discuss each of them in detail offering our best strategy for reliable and robust estimation.

First, reverse causality arises where government entities implement GPP in response to a competitive market environment in order to stimulate innovation, attract environmentally conscious suppliers, or improve public perception. In other words, competitive markets would cause the choice of GPP rather than the other way around.
Policy Research Working Paper 10443

# **Abstract**

Routine immunization coverage estimated in surveys often substantially differs from figures reported in administrative records, presenting a dilemma for researchers and policy makers. Using high-frequency phone surveys and administrative records from government sources in 36 low- and middle-income countries, this paper shows that such misalignment has also been common in the case of COVID-19. Across the sample, survey estimates exceed administrative figures by 47 percent on average, at times suggesting markedly different policy conclusions depending on the data source consulted. This pattern is particularly stark and consistent in Sub-Saharan Africa. To investigate the sources of this discrepancy, the paper presents results from six methodological experiments that vary survey design choices and documents their effect on estimated COVID-19 vaccine coverage. The results show that design choices matter, in particular the selection of respondents to be interviewed. However, phone survey estimates prove remarkably robust to several commonly claimed biases. After accounting for observed errors of representation and measurement in the survey data, there remains a nonnegligible, unexplained residual gap with administrative records. The paper provides indicative evidence of flaws and weaknesses in administrative data recording and reporting that affect reported vaccination rates and could contribute to this gap. The findings matter for past research on COVID-19 vaccination, future immunization efforts, and the design of robust data production systems on health topics.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ymarkhof@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the
and communications technology. Overall, they confirm that productivity is, among other factors, determined by infrastructure quality. However, they also show that different infrastructure types matter more in some countries than in others. Low‐quality electricity, measured by several power supply and outage frequency indicators, reduces log productivity in Zambia by 34.1% on average, but only by 0.3% in Morocco. This difference is likely driven by a lower overall influence of infrastructure on productivity in the Moroccan economy and a higher reliability of the Moroccan power grid.

Similar effects have been shown for garment and food‐processing firms in the Kyrgyz Republic, Moldova, Poland, Tajikistan, and Uzbekistan (Bastos and Nasir, 2004). Using Enterprise Survey data for eight countries in Eastern Europe, Anos‐Casero and Udomsaph (2009) show that over the period 2001 to 2004, an increase of one standard deviation in infrastructure quality, measured by an indicator combining information on power and water outages as well as mainline telephone disruptions, raises the total factor productivity of the average firm by 9.8%.

## **More reliable electricity supply can increase output and productivity**

Empirical studies for a large number of countries have shown that power supply irregularity can have substantial adverse impacts on the productivity and sales of firms. These studies are reviewed in this section and are summarized in _Figure 2_ . A detailed overview of studies is offered in the Annex, providing the case study country, the type of firms considered in the analysis, details on the power supply quality indicator, methodology, and the estimated effect of power supply irregularity on different indicators of firm performance.

7
Policy Research Working Paper 10514

# **Abstract**

Using detailed monthly firm-level trade data from Indonesia from February 2019 to June 2021, this paper shows that firm-level exports were overall more resilient than imports during Covid-19. Firms that participated in global value chains were more resilient to the Covid-19 shock beyond the immediate short-run compared to firms that did not. However, among global value chain firms, those that faced certain types of non-tariff measures on their import products, notably port of entry restrictions, on average faced larger reductions in export quantities and number of transactions compared to firms that did not face such restrictions, consistent with the evidence of major port congestion during Covid-19. Therefore, although international connectedness could be a source of vulnerability to global shocks in the immediate short run, policies that enable firms to be more globally engaged through global value chains could enhance resilience. Relatedly, tackling measures such as port of entry restrictions can ensure fast and efficient port and customs procedures, especially during periods of high port congestion, as global value chain trade requires goods to cross borders many times.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice and the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at amontfaucon@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and
) modeling methods and (2) aggregating over a range of years. An example of modeling methods closely related to this study is the UNICEF-WHO-World Bank joint child malnutrition estimates (JME) (UNICEF, WHO, and the World Bank 2018). The JME adopts linear mixed-effect models allowing for random effects at the country level and for heterogeneous covariance structures. One model is fitted for each region or country group for calculating its aggregated number. Such modeling methods are beyond the scope of this study. For simplicity, we chose to calculate regional averages for a range of years following the exercise in Nguyen and Wodon (2015). Thus, we create regional averages for five-year periods: 1993-1997, 1998-2002, 2003-2007, and 20082012. We use these five-year periods so that the middle years of these ranges,1995, 2000, 2005, 17
In short, based on the existing evidence, we do not consider the relative poverty hypothesis further, since we do not expect the cost of eliminating poverty to be correlated with mean income.

A further hypothesis relates to access to technology for supporting SP programs:

**_Technology: The ICT hypothesis._** _The governments of poorer countries have less access to ICT, which makes SP programs more costly options for public spending._ The literature on SP has not often discussed this issue, but it is a seemingly plausible assumption that SP programs <mark>tend to be ICT-intensive, such as in creating and maintaining social registries of participants and</mark> payment and monitoring systems. There is evidence that such ICT capabilities enhance the quality and coverage of SP programs in poor countries.<sup>16</sup> Better communication infrastructure in a country can facilitate new options for using mobile money technologies that have proven to be <mark>helpful for cash transfer delivery in some low-income settings.</mark><sup>17</sup> <mark>However, with limited access to</mark> ICT at the national level due to poor infrastructure, the cost of implementing SP programs can be expected to be higher in poorer countries, leading policy makers to substitute SP in favor of other types of public spending. We will test this hypothesis using ICT usage indicators at the national level over time.

The next hypothesis concerns population aging:

**_<mark>Demographics: The aging hypothesis.</mark>_** _<mark>Richer countries tend to have older populations,</mark> which increases the demand for SP._ The fact that richer countries tend to have more elderly populations follows almost immediately from the well-known Preston (1975) curve, whereby life expectancy at birth tends to be an increasing (concave) function of average income across countries.<sup>18</sup> <mark>With an older population in richer
_Use the regional distribution._ Finally, for close to 3% of the world’s population (50 countries), we have no prior household survey data. For those countries, we generate the 2020 distribution in the same way as we generated the 2019 distribution: by applying the regional distribution to those countries. This follows the methods used by PIP for countries without data.

# **4. Results**

In what follows, we first report within-country changes in poverty and inequality comparing across the four preferred methods – actual household survey data, data from NSOs, data from phone surveys, and data from the literature.<sup>15</sup> Then, we report the changes in global inequality and poverty. Finally, we look at the drivers of these global changes.

## **4.1 Impact of COVID-19 on inequality and poverty within countries**

Figure 1 shows the net percent change in the Gini index in 2020 for all countries with data from our four preferred methods. The net impact is calculated as the difference between the Gini index in the observed 2020 distribution and the counterfactual distribution.<sup>16</sup> The countries are ranked according to their mean income or consumption expenditure on the horizontal axis. In general, changes in the Gini index in 2020 are mixed and, for most countries, small. Note that some of the larger declines in the Gini – for instance in the US, Canada, Australia, Brazil, and South Africa – can be linked to the wide-ranging social assistance programs that were implemented in 2020. Across countries, inequality is just as likely to increase (in 43 countries) as decrease (in 48 countries).

Whereas the changes in inequality were mixed, extreme poverty has largely been increasing (Figure 2). We find large increases in extreme poverty for countries that already had high poverty rates. This could either be because these countries experienced larger shocks to average incomes, or because they have more people just above
we are aware that by disregarding non-OECD immigration countries, we probably underestimate the brain drain for a dozen of developing countries (such as Egypt, Sudan, Jordan in the neighborhood of the Gulf States, Namibia, Zimbabwe and other countries which send emigrants to South Africa, etc.).

Another point is that, by focusing on census and register data, our methodology does not capture illegal immigration for which systematic statistics by education level and country of origin are not available. Because illegal immigrants are mostly less educated, we obviously overestimate the average level of education of the immigrant population. Nevertheless, this limit should not signi...cantly distort our estimates of the migration rate of highly skilled workers.

Only taking into consideration the population aged 25 and over maximizes the comparability of the immigration population with the Barro and Lee data on educational attainment in source countries. It also excludes a large number of students who emigrated to complete their education. We cannot control for graduate students aged 25 and over completing their schooling<sup>7</sup> . Note that, in a limited number of OECD countries identi...ed below, we are unable to distinguish between young and adult immigrants.

A related problem is that we have no information on the age of entry. It is

> 7Carrington and Detragiache (1998) used data from the Institute of International Education to estimate the number of graduate students completing their schooling in the United States. We consider that some of these students aged 25 and over receive grants and can be considered as workers (researchers).

8
States). The premium reflects the potential volatility of investments in a given country due to defaults associated with political or other events. Country risk premiums are usually estimated as the average spread over U.S. Treasury bonds (assumed to be riskfree) of U.S. corporate bonds with a credit rating equivalent to that of the country under consideration (called the _default spread_ ). To estimate these spreads, we used default spreads estimated by Reuters for a large number of utilities worldwide.<sup>9</sup> However, Reuters does not calculate this default spread for a number of developing countries such as, but not limited to Mozambique, Cameroon, Georgia and Estonia. As a result, for these countries, we utilized Fitch, Standard & Poor or Moody’s ratings where available and then, proxied these default spreads using rating equivalences published by Moody’s.

The country risk premium is the most discriminating factor among countries, ranging from less than 1 percent in Chile and South Africa to 13-15 percent in Argentina, Kenya and Venezuela. It is also highly volatile—varying, for example, from 8 percent in 1998 to 13 percent in 2002 in Argentina. This is because it is influenced by many factors subject to frequent shocks and variations, including exchange rate risk, political risk, and regulation risk. It is for this reason that one needs to make sure the calculation compares investors’ expected

> 9 Some authors argue that the country risk premium is likely to be higher than the country’s default spread. Instead, they multiply the default spread by the ratio of the volatility of the equity market to that of the bond market in the country under consideration (sometimes proxied by the same ratio globally of 1.5). To be as conservative as possible, we have not made such an adjustment

7
To study the development consequences of forced displacement in Venezuela, we approximate annual municipal outflows using data from Colombia, the main host of displaced Venezuelans. This is necessary since autocratic regimes typically do not produce reliable data on forced displacement outflows. Annual variation comes from the total number of Venezuelans registered as entering Colombia each year. Municipal variation comes from the municipality of origin shares of Venezuelans living in Colombia, drawn from the Venezuelan Refugee Panel Study (VenRePS). This was collected in 2018 at the peak of the displacement crisis and is representative of Venezuelans living in Colombia at that time (Ib ́anez et al., 2025). The interaction between these two components distributes total national outflows across Venezuelan municipalities of origin and is scaled by municipal population in 1990 to express the measure as a share of baseline population.

Our empirical strategy cannot compare development outcomes across municipalities with high and low displacement outflows because individuals tend to leave certain areas for reasons that are likely correlated with local economic trends. We therefore construct an instrumental variable for displacement outflows that exploits municipal variation in foreign settlement shares before the onset of the crisis. The share of foreigners living in each municipality comes from the last population census before Ch ́avez’s presidency, collected in 1990. Foreigners in Venezuela in 1990, predominantly Colombians, likely lowered displacement costs by providing network support and information to prospective migrants. Consistent with this mechanism, there is a strong positive correlation between the 1990 municipal foreigners share in Venezuela and the municipal origins of Venezuelan migrants living in Colombia in 2018. We validate this mechanism further using Venezuela’s National Surveys on Living Conditions (ENCOVI), collected between 2017 and 2021, showing that municipalities with a higher 1990 foreign share were more likely to have households reporting relatives in Colombia.

Our shift-share instrument generates annual municipal variation in displacement intensity, interacting pre-crisis foreign settlement shares with national displacement outflows to Colom4
# **6.1 Governance Issues for ICBs and Independent Utility Regulatory Agencies**

Much of the literature on ICG governance concentrates on similar issues to those discussed in the utility regulation literature eg quality of laws, appointment and dismissal criteria for ICB governors, etc. There has also been some explicit investigation of the role of _transparency_ . This is true both in the narrow sense (ie the impact of ICBs providing justification for their interest rate decisions) and in a wider sense.

For the wider sense of “transparency”, Geraats (2003) – following Eijffinger and Geraats (2002) and their survey of nine formally independent OECD central banks – identifies five different elements of transparency.

These are defined as follows: <u>Eijffinger and Geraats (2002): Central Bank Transparency</u> _1. Political Transparency_ – Openness about policy objectives

- Formal objectives for monetary policy

- Quantification of primary objective(s)

- Explicit contractual arrangements or contracts between monetary authorities and central bank

_2. Economic Transparency -_ Economic information used

- Availability of basic economic data

- Disclosure of formal macroeconomic models used for policy analysis

- Publication of central bank macroeconomic forecasts

_3. Procedural Transparency_ – The way monetary policy decisions are taken

- Explicit policy rule or strategy

- Comprehensive and timely explanation of policy decisions

- Disclosure of voting behaviour

_4. Policy Transparency_ – Disclosure and explanations of decisions

- Prompt announcement of decisions

- Explanation to accompany announcement

- Future policy inclination

_5. Operational Transparency_ – Implementation and evaluation of policy

- Frequent evaluation of extent to which policy targets are met

- Regular provision of information on unanticipated disturbances

- Regular evaluation of policy outcomes in light of objectives.

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