rafmacalaba/data-use-mentions-extended huggingface dataset:
components across countries?

We make multiple contributions to the literature, which has until now focused on advanced economies. This paper is the first study to separate technology and non-technology drivers of productivity across a broad range of countries using a new spectral SVAR approach introduced by Dieppe et al. (2019, 2021). Panel and individual VARs are estimated and identified for over 100 advanced and emerging economies, an unprecedented country sample relative to the existing literature which has generally focused on the G-7 advanced economies.<sup>5</sup> This paper also uncovers non-technology shocks that may have persistent effects on advanced economy and EMDE labor productivity levels through the capital-deepening channel and country-characteristics that govern the degree of persistence. We are also the first to assess the synchronization of productivity growth across a broad range of countries for measures that remove non-technology drivers of productivity

> 3A survey of the SVAR literature has found that technology shocks account for between 1 and 55 percent of variations in output in the US (Ramey, 2016).

> 4While not explicitly modeling demand shocks, a range of studies have documented the existence of lasting negative effects on output and productivity from financial, currency, and political crises (Dieppe, 2020; Cerra and Saxena, 2008; Jord`a et al., 2013; Reinhart and Rogoff, 2014).

> 5Previous studies have focused on a small subset of advanced economies. For example, Elstner and Rujin (2019); and Gal ́ı (1999) apply long-run restriction-identified SVARs to G-7 economies only.

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(2012)|Male|54%|(-47%, +82%)|22.7%|(-31%, +76%)|
|<br>Valdivia (2012)||||||
|General training|Female|n.r.|n.r.|9%|(-8%, +29%)|
|Training+ technical assistance<br>_Notes:_|Female|n.r.|n.r.|**20.4%**|(+6%,37%)|

<mark>95% CI denotes 95 percent confidence interval. Impacts significant at the 10% level or more reported in</mark> **<mark>bold.</mark>** <mark>n.r. denotes not reported.</mark> (a) They look at an aggregate sales and profitability measure and find no significant impact for either gender. (b) Impact on profit from main product.

(c) Calculated as difference-in-difference calculation. Study reports difference in log sales is significant at the 1% level. Profit increases are scaled as a percentage of the control group mean to enable comparability. When multiple rounds are used, longest-term impacts available are reported.

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such regimes the availability of substantial resources is more likely to be channeled in ways that deter rebellion (the military and rents), plausibly countering the grievances generates by natural resources and rebels’ viewing of such resources as a prize for taking over the state.

Following the theoretical discussion we derive two hypotheses: _H1: Presence of natural resources increases the risk of civil war for semi-democratic and_ _unstable regimes and states where leaders appeal to ethnicity in order to govern._ _H2: While coup risk increases the risk of rebellion, natural resource rents allow high coup d’état risk regimes to lower the risk of civil war._

# **3. Data and research design**

We use two alternative definitions for civil war onset, based on widely used measures in the literature: Fearon and Laitin (2003) and the Prio/Uppsala data (Gleditsch et al. 2002) for years 1946-2003. The Fearon and Laitin definition captures the onset of large scale civil wars, coded using a threshold of 1000 battle deaths over the course of conflict with a yearly average of at least 100 deaths. This data is updated to 2003 using Fearon (2011). The Prio/Uppsala measure captures the onset of smaller scale conflict, earlier onsets for the large scale conflicts and flareups of violence in a dormant conflict. This definition uses a lower threshold of 25 battle deaths per year and we use the Lujala (2010) as a data source. The dependent variable is coded 1 for country years with a civil war onset and 0 in all other cases, and we do not eliminate the observations with an ongoing civil war.

We attempt to measure state weakness in ways that capture the theoretical account. To capture mixed regimes we use the same indicator variable for anocracy as Fearon and Laitin (2003) based on Polity IV scores. Anocracies have Polity IV scores between -5 and 5. Also similar to Fearon and Laitin (2003), institutional instability is coded 1 if a country has 15
constraints limit their ability to help their children achieve their hopes and aspirations for them, refugees are more able to express clearly articulated navigational capacity than hosts which we think may due to the selection and learning effects associated with surviving violence and finding their way to the refugee camp.

The second study, “The National Poverty Study” (Alexander et al, 2017) is based in the United States and is conducting a “qualitative census of urban and rural poverty” by interviewing 5,000 poor and near-poor respondents, sampled to be representative of poor households in in urban and rural settings in the United

> 6 See also this interesting effort to use video and audio images to visualize poverty in India: https://onehundredhomes.in/.

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community poverty status. Within each stratum, a random sample totaling 46 districts was drawn.<sup>9</sup> Five schools were randomly selected within each district.<sup>10</sup> The resulting sample was nationally representative of Egyptian schools with KGs.

A total of 214 schools were sampled for the diagnostic research tool.<sup>11</sup> A random sub-sample of 115 of those schools were selected for the QAS tool data collection effort, with 55 initially randomly assigned to data collection firm enumerators and 60 assigned to MoETE supervisors. Due to limited availability of MoETE supervisors, 18 schools were reassigned to the data collection firm enumerators and five were not collected (due to their location and insufficient availability of data collection firm enumerators in that location).

Data were collected for up to three KG1 and three KG2 classes per school (randomly selected if more than three). A total of 333 classrooms were sampled across schools. Since there was often more than one teacher per class, this led to a sample of 434 teachers consenting to be interviewed and responding to both tools.<sup>12</sup> A random sample of four children per classroom was selected (1,332 children therefore should have been sampled) and 1,169 children consented and responded to both tools.<sup>13</sup> The data collection firm tried up to three times to reach parents, based on phone numbers provided by the school. For the parent data, there was substantial non-response (primarily that parents did not pick up, but some refusal when reached). Ultimately, 625 parents were reached and consented to be interviewed. Weights are used in all our analyses. The weights account for the original sampling design and non-response.<sup>14</sup>

# **_4.3 Outcomes_**

Our primary outcomes are the consistency of items or dimensions across the two tools, which are essentially an issue of measurement error. Reliability (internal
# _Solvency versus Liquidity_

The seriousness of insolvency can be gauged from two vantage points. First, what are the market signals? If bond spreads indicate a high probability of default and keep rising even as official bailout discussions continue (the case with Greece 2010, Russia 1998 and Argentina 2001), then this should be taken as a red flag. Second, what are the country‘s fiscal and growth prospects as conveyed by an assessment of its fundamentals by economists at the IFIs? How likely is it to generate the needed primary surpluses to stabilize and even lower debt? If the chances are dim, accompanying official loans at the risk-free rate with an equal upfront haircut for all investors will then enable more credible fiscal targets and lead to lower long-bond spreads, minimizing reputation costs.

However, it should be admitted that it is not always easy to judge whether the country is dealing with a multiple equilibria (liquidity and confidence) or a fundamental problem (insolvency). Brazil in the summer of 2002 just before Luiz Inacio Lula da Silva, the candidate of the Brazilian Workers‘ Party, was elected president is a case in point. Bond spreads reached 2000 basis points that July as presidential election polls ―...indicated that Lula would win the presidential election....can [investors] be certain that a Brazil run by a president with a past record of sympathizing with default will not take the easy way out?‖ In the article from which this quote is taken, Williamson (2002) argued that fundamentals were sound: primary fiscal surpluses had been raised substantially and budget constraints hardened for the state governments. Besides, the real had been floated in 1999.<sup>40</sup> In his classic on multiple equilibria, Obstfeld (1994) recognizes: ―Ultimately,....crises based on limited foreign reserves [liquidity] must also be based on overall fiscal weakness:

[otherwise]...it would be .....feasible to borrow sufficient
We did not use post-tax income because few Malaysians are subject to personal income tax; it is thus unlikely to affect poverty or vulnerability status.

Income data have been adjusted for spatial price differences. We constructed a spatial price index that reflects prices of basic goods, services and housing at the state level, separately for rural and urban areas within the state (see Table A 1 in the Appendix). The index was derived from the household-specific poverty lines that the Department of Statistics Malaysia calculates for each survey year, which depend on the location and demographic composition of the surveyed household.<sup>18</sup> As explanatory variables in our income model, the method requires we only use variables that are time-invariant. We use three such categories. First, we employ dummy variables for the birth cohort of the household head, using five-year bins. The first bin starts in 1949 (those 55 in 2004) and the last bin starts in 1984.<sup>19</sup> Second, we use six dummy variables for the highest level of education enjoyed by the household head. Those with primary education or less form the base category. Third, we use dummy variables for the ethnicity of the household head. The four categories are Bumiputera, Chinese, Indian and Other; Bumiputera is set as the base category.

In addition, we use two location dummies that, strictly speaking, are not time-invariant. These are one dummy for urban or rural location and another for being located in either Peninsular Malaysia or East Malaysia. This restricts the interpretation of results to households that did not migrate from one such classification to another.<sup>20</sup> We opted for this approach because omitting these dummies led to biases in predicted income based on urban or rural location, which would have led us to overestimate downward mobility for rural households. Table A 2 in the Appendix provides the regression results of our income models for each survey year, based on the subsample of households with heads in
Zimbabwe (2005-2006) (Lesotho Government and ORC Macro, 2005; Swaziland Government and ORC Macro 2008; Zimbabwe Government and ORC Macro, 2007).

In each country, the DHS uses a two-stage sampling design. In the first stage, the clusters are selected from a list of enumeration areas from the latest national census (e.g. the 1996 Lesotho Population Census). In the second stage, a complete listing of households is created in each selected cluster. In each household, randomly selected from the complete listing, all women aged 15-49 who were either usual residents or visitors present in the household on the night before the survey were eligible to be interviewed. For the male survey, only a fraction of the sampled households were selected. In this subsample, all men aged 15-59 years were eligible to be interviewed if they were either permanent residents or visitors present in the household on the night before the survey. Our final sample includes 9,892 individuals in Lesotho, 9,143 in Swaziland and 16,082 in Zimbabwe.

One of the great advantages of the DHS is that it includes HIV test results. All women and men eligible to be interviewed were asked to voluntarily provide a blood sample for HIV testing. The 2006-2007 Swaziland DHS estimated that 31.3 percent of women between 15-49 years and 19.5 percent of men between 15-59 years are HIV positive. These figures are slightly lower in Lesotho, with an HIV prevalence of 26 percent among women and 18.5 percent among men in the same age group. In Zimbabwe, DHS data report that 20.7 percent of women are HIV positive compared to 14 percent of men. The difference in HIV prevalence between genders is common to many other subSaharan African countries,<sup>7</sup> reflecting, on the one hand, a higher biological probability of transmission from men to women than from women to men and, on the other, a disparity due to sociological factors including economic resources and power differentials in sexual relationships
The RISE data can further be disaggregated into renewable energy (mostly on-grid) and energy access. We therefore correlate these two measures with investors that are active in the respective aforementioned sector. It should be mentioned that for some countries there are few data points, which reduces the overall correlation coefficient. Figure 16a shows that there is a positive correlation between the assessment of the grid investors and the RISE renewable energy data (R<sup>2</sup> of 0.13 for a sample of 35 countries). For countries with data points the assessment of the investor is spread around the trend line on both sides in equal fashion, indicating that overall, the index is correlated well the investors’ perception. The off-grid RISE data tend to be more positive about countries than the investors’ perception (as many countries are above the trend line) and also exhibit slightly lower correlation (R<sup>2</sup> of 0.11 for a sample of 35 countries).

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Bank, Washington, DC.

- ———. 2009c. ―Enterprise Surveys for Cameroon 2009.‖ Washington, DC: World Bank.www.enterprisesurveys.org/ExploreEconomies/?economyid=34&year=2009.

- ———. 2011b. _Getting Electricity—Database_ . Washington, DC: World Bank.

- World Bank and IFC (International Finance Corporation). 2010. ―Project Appraisal Document for the Development of the Kribi Power Development Company.‖ World Bank and IFC, Washington, DC, February 23.

# Transport

- Bofinger, Heinrich C. 2009. ―An Unsteady Course: Growth and Challenges in Africa’s Air Transport Industry.‖ AICD Background Paper 16, Africa Region, World Bank, Washington, DC.

- Briceño-Garmendia, Cecilia, and Nataliya Pushak. 2010. ―The Republic of Congo’s Infrastructure: A Continental Perspective.‖ AICD Country Report, Africa Region, World Bank, Washington, DC.

- Bullock, Richard. 2009. ―Off Track: Sub-Saharan African Railways.‖ AICD Background Paper 17, Africa Region, World Bank, Washington, DC.

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**Figure 11: Tax maneuver and formalization based on behavioral simulations (with and without random component). Relative change in consumable income w.r.t. no-reform scenario by decile, age group, household and location types.**

<!-- Start of picture text --> 2<br>1<br>0<br>-1<br>2<br>1<br>0<br>-1<br>no random component (1% formalization) with random component (6.2% formalization)<br><!-- End of picture text --> Source: Authors’ calculations using RUSMOD for 2017 policy year and 2016 RLMS-HSE data.

# V. Conclusion

Microsimulation models such as RUSMOD are very versatile tools with multiple potential applications. Such models can be used, for example, to: (i) analyze existing policies in terms of who is gaining from the system, and who is a net payer; (ii) analyze policy reform proposals in terms of how they may affect various population groups (for example, to ensure that low-income households are protected), and what are their consequences for poverty, inequality or budgetary outcomes; (iii) calibrate policy packages to meet specific criteria, such as revenue-neutrality; (iv) provide a counterfactual for comparisons, where only policies are changed, not the economic circumstances, or vice versa; (v) “nowcast” the income distribution, where changes to the income distribution due to policies can be estimated in real time via simulations, rather than on the actual data that takes time to arrive; (vi) compare actual vs statutory performance of various programs (for example, to estimate tax avoidance); and (vii) “switch on” or “switch off” single policies to isolate and understand their impact, or “borrow” selected policies from other countries where they are known to perform well.

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products. Additionally, the lack of sufficient competition for financial service providers and less awareness by consumers increases the degree of asymmetric information in underdeveloped financial markets, leading to a disadvantage on the part of consumers. Hence, an effective financial consumer protection framework is a key component of financial inclusion strategies.

In recent years, financial inclusion gained prominence in the development agenda. According to CGAP (2009), only 30 percent of adults in developing countries are estimated to have access to basic deposit services and even fewer to credit, insurance and other financial services. The consequence of this is that the poor have to rely on more costly informal financial services to save and to borrow. This inequality of opportunities represents an obstacle to economic development. Effective consumer protection regulations and strengthening of financial capability promote equal access to financial services for all by reducing information asymmetries, enhancing competition and innovation, and increasing consumer participation in the financial system.

The literature on the broad topic of consumer protection is abundant. Yet, there are only a handful of comprehensive studies on the topic of consumer protection in financial services.<sup>2</sup> This paper contributes to the debate by providing an overview of financial consumer protection issues in the existing literature, and by providing empirical analysis on the links between consumer protection and financial sector outcomes, drawing on the new unique cross-country data set on financial consumer protection. To the best of our knowledge, this study is the first to systematically collect and analyze cross-country data on consumer protection regulations and policies in financial services. The data used in this paper come from a survey of financial regulators from over 140 countries conducted for the annual _Financial Access_ survey by CGAP and the World Bank Group in 2010.<sup>3</sup> The survey and the paper only cover credit and deposit services, and do not include investments, pensions, insurance or other financial products.

Broadly, the results of the survey on financial consumer protection in deposit and loan services reveal that most countries have some form
countries use income—for example, in SSA only those upper-middle-income countries located in the Southern Cone use income data.

When in high-income countries we compare consumption to income inequality, this latter tends to be higher (Heathcote et al. 2010; Aguiar and Bils 2015); in other countries, due to data limitations, this comparison is not always feasible (Devarajan 2013; De Magalhães and Santaeulàlia-Llopis 2018). Nonetheless, a quick overview of Povcalnet enables us to identify 71 cases where inequality measures are available for the same country and same year both for consumption and income.<sup>9</sup> Figure 2 plots the differences in the Gini index (panel a) and the 90th percentile over the 50th percentile ratio (P90/P50panel b) for these cases. In about 20 percent of the cases, the Gini based on income is slightly lower than that measured on consumption, but in the remaining 80 percent of the cases is higher. On average, income inequality is higher than consumption inequality by 10 percentage points, and in 40 percent of cases Gini measured on income exceeds that based on consumption by more than 10 percentage points. Interestingly, the gap between income and consumption slightly increases when looking at the P90/P50 ratio (panel b). In this latter case, the average gap between the two measures is 11 percentage points and about 30 percent of the observations have a gap higher than 20 percentage points. Indeed, the gap between the two measures tends to be more accentuated on the upper tail of the distribution rather than around the means.

In order to deeply inquire into this aspect, we estimate a model following the general formulation taken from the literature ( <mark>Förster and Tóth 2015)</mark> that has enough explanatory variables but, at the same time, does not cover all the inequality determinants, because these are not easily available for many developing countries and for all the years we analyze.<sup>10</sup> Because the scope
# **A2. Macroeconomic Variables**

The source of each macroeconomic variable is World Development Indicators published by World Bank.

**GDP per capita (constant LCU):** GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources.

**GDP growth (annual %)** : Annual percentage growth rate of GDP at market prices based on constant local currency. Aggregates are based on constant 1995 U.S. dollars. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources.

**Inflation, consumer prices (annual %)** : Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a fixed basket of goods and services.

**Real interest rate (%):** Real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator.

**Domestic credit provided by banking sector (% of GDP):** Domestic credit provided by the banking sector includes all credit to various sectors on a gross basis, with the exception of credit to the central government, which is net. The banking sector includes monetary authorities and deposit money banks, as well as other banking institutions where data are available (including institutions that do not accept transferable deposits but do incur such liabilities as time and savings deposits).

# **A3. Definition of Liberalization**

The liberalization process is defined as follows by Kaminsky and Schmukler (2003).

**Liberalization of domestic financial sector