rafmacalaba/datause-encoder-data huggingface dataset:
OA4 contains two examples of market-designed key facts statements in Peru.<sup>2</sup> The APR, for example, appears around the center in the 10<sup>th</sup> row for one institution but in the top left corner for the other. Other terms also appear in different places, making the comparison between both products difficult.

This study seeks to understand the role of disclosure formats in facilitating comparison shopping for savings and credit products by low-income consumers in Peru and Mexico. These countries were chosen because they both have similar levels of financial access but have de jure regulations with different transparency requirements. According to the market conduct index published by the Economist Intelligence Unit’s 2014 Global Microscope Index and Report, Peru is ranked second while Mexico is ranked 25th (middle of the sample of countries).<sup>3</sup> We implement a laboratory experiment in which low-income consumers were assigned a profile and then incentivized to choose the product that best fit their needs from among 5 or 10 products. In each round of decision-making, information about the products was presented in a different format, including current marketing materials gathered from financial institutions during sales visits by actual consumers and a simplified key facts statement (KFS) designed using behavioral insights to facilitate comparison shopping.

Our results show that the simplified KFS with its standardized format significantly improves consumer decision-making compared to the marketing materials currently provided by financial institutions. The effects are however much stronger for credit than savings products. The probability of choosing the cheapest loan increases from 42 percent using the marketing materials to 65 percent using the simplified KFS but it only increases from 32 percent to 34 percent for savings accounts. One reason is perhaps that individuals may not care about the total yield of the savings account and focus instead on other characteristics. After all, a consistent finding from the literature on savings is that

> 2 A product key facts statement (KFS) is a document that provides concise information of the key features and risks of a financial product.

> 3 Market conduct includes indicators of the capacity to protect the financial consumer, the content of disclosure rules, the disclosure of product terms, pricing information and non-discrimination in the financial service provision.

4
While national service flows are based on national input-output data, the data on services trade flows come from a number of sources. The OECD, Eurostat and the UN all provide data in some form on bilateral services trade flows (both imports and exports), by partners and BOPs (balance of payments sectors) for up to 24 sectors and subsectors. The most comprehensive coverage of reporting countries among the three sources is UN, which at present count provides data on roughly 190 reporters. Eurostat and OECD provide data for a more limited number of reporters, though with more sector detail than the UN. Eurostat covers 27 EU members plus Croatia, Iceland, Japan, Norway, Turkey, Switzerland, and USA, while the OECD covers 28 countries (all the OECD members apart from Chile, Iceland, Israel, Slovenia, and Switzerland). Time coverage is the deepest with EUROSTA. IMF data cover almost as many countries as the UN, and for a longer time span, but only for trade with world.<sup>3</sup> Within GTAP, the efforts to reconcile services balance of payments data with national input-ouput data are well documented, and reflect innovative approaches based on information theory and entropy methods to flesh out the pattern of trade enough to allow construction of a global dataset for estimating cross-border trade in services. (See MacDougall and Hagemejer 2005, and Van Leeuwen and Lejour 2006). For the transport sector, reconciliation of trade data is explicitly linked to the parallel reconciliation of data on trade in goods, as services play a critical role in bridging the gap between reported export flows for goods, in one country’s national accounts data, and reported import flows in another country’s data (Gelhar 1996).

The structure of the data provides a comprehensive and consistent record of national income accounting relationships between different sectors and regions. It is based on a fundamental, general equilibrium principle of economics – every income (receipt) has a corresponding expenditure (outlay). The strength of this framework is that it provides a comprehensive and consistent record of the interrelationships of an economy, including intermediate and final demand linkages. For our purposes, it offers the advantage of linking consumption and external trade patterns explicitly to the inter-industry structure of intermediate demand. This allows for a fuller analysis than is possible when working strictly with input-output tables.

> 3See Francois and Pindyuk (2010).

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# Table 9: Age of Household Head and Inequality, Malawi (ISA 2010)

## (A) Rural Residency

<u>(A1)</u> Population Shares <u>(%)</u> by Age Groups

||15-24|25-34|35-44|45-54|55-64|65+|
|---|---|---|---|---|---|---|
|Population 2010|9|29|22|15|11|13|
|(A2) Average US$|by Age|Groups|||||
||15-24|25-34|35-44|45-54|55-64|65+|
|Consumption|1137|1379|1559|1526|1347|1008|
|Income|868|1073|1294|1327|1106|823|
|Wealth|639|965|1399|1604|1699|1484|
|_▷_Land|336|471|674|711|660|522|

## (B) Urban Residency

### <u>(B1)</u> Population Shares <u>(%)</u> by Age Groups

||15-24|25-34|35-44|45-54|55-64|65+|
|---|---|---|---|---|---|---|
|Population 2010|8|40|25|13|7|6|
|(B2) Average US$|by Age|Groups|||||
||15-24|25-34|35-44|45-54|55-64|65+|
|Consumption|1854|2390|3385|3684|3226|2728|
|Income|939|1858|3401|3623|2023|2850|
|Wealth|933|1488|3413|6247|6661|3409|
|_▷_Land|311|389|458|233|928|371|

_Notes:_ All variables are averages in current USD. The construction of the measures of household consumption, income, and wealth is discussed in Section 2, with further details in the online appendix.
induce private sector underinvestment. A direct implication is that insofar as public investment resolves a market failure by crowding in private investment, it should be allowed a lower marginal rate of return compared to when capital markets are perfect (Ogura and Yohe, 1977).

dollar increase in public investment raises private investment by about 1.6 dollars.<sup>6</sup> This is because public and private investment are complements, which implies that an increase in public investment raises the marginal product of private investment and hence, crowds-in private investment.<sup>7</sup> The evidence of crowding-in of private investment by public investment holds in LICs and SSA. Specifically, a dollar increase in public spending raises private investment by 1.5 and 1.4 dollars in LICs and SSA, respectively. Our estimated crowding-in effect in a smaller sample of 39 LICs is smaller than those estimated by Eden and Kraay (2014), who found that government investment raises private investment by approximately 2 dollars. Our results are, however, starkly different from findings in Boehm (2020), who provided evidence of crowding-out of private investment in OECD countries. We also find evidence that the crowding-in of private investment increases over longer horizons. More precisely, the crowding-in effects are visible for up to three years. Finally, we find that public investment is more effective in crowding-in private investment in countries with strong control of corruption control. Also, the crowding-in effect of public investment is greater than one and statistically significant in countries with lower financial development.

effective vehicle to address underinvestment issues induced by capital market distortions. This is because institutional reforms that aim to resolve market distortions take time to implement. Consequently, while these reforms are the first best solution to solving market distortions, the reform process can be combined with increases in public investment to address private sector underinvestment. Our findings are also a warning to policy makers that cutting public investment may undermine private investment.

The rest of the paper proceeds as follows. Section 2 presents a simple theoretical model linking public investment to private investment. In section 3, we discuss the empirical strategy used to quantify the crowding-in effect and present summary statistics, stylized facts of our investment data and instrumental variable, and. Section 4 presents the main results. Section 5 presents sensitivity, validity, and robustness exercises. In section 6 we

> 6 Boehm (2020) provided evidence of crowding-out of private investment in OECD countries.

> 7 raises the marginal product of private investment and hence, the level of private investment. Real world examples of such complementary public investment include: the construction of a highway linking rural and urban centers, which increases productivity of private capital; safe public water delivery, which can induce higher private investment in sanitation; and reliable irrigation, which can improve private agricultural activities.

4
**Figure 1: Kernel density smoothing of propensity score across treatment and control samples**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>propensity score<br>Treatment Control<br>5<br>4<br>3<br>2<br>Kernel density<br>1<br>0<br><!-- End of picture text --> Table 2 shows the balance at baseline for the trimmed sub-sample across a range of the covariates. The sample at baseline is highly unbalanced before IPW, signaling the statistical challenge to adjust for these differences (Imbens 2015): beneficiaries live in larger households with more children and fewer working age adults, lower consumption per capita, and higher scores for negative coping strategies indices. After matching these differences are eliminated. A test of joint-orthogonality for the entire set of covariates shown in the balance table returns a p-value of 0.897. Baseline balance in the matched sample is maintained across survey waves, despite high rates of attrition (Appendix Table 3).<sup>20</sup> Since causal identification of program effects hinges on conditional independence, it is useful to consider how similar households ended up with different beneficiary status at baseline. As described in Section 3, the study sample was randomly drawn from the pool of ESSN applicants, all of whom submitted applications between November, 2016 and May, 2017. Upon the receipt of the application, the information declared by the household is cross-checked and verified digitally through the databases integrated in the ISAS system. While this system was best practice and hard to game, there could be discrepancies between a household’s demographic characteristics at baseline and what was registered in the underlying databases (PDMM, MERNIS). As described in detail in Section 2, such discrepancies could exist due to the absence of documentation, spelling differences in existing documents, a backlog of cases to be processed, or difficulties in linking registrations of household members. This means that households that appear _de facto_ eligible at baseline could be deemed _de jure_ ineligible by the program administration and vice versa. We exploit these discrepancies to match households that appear very similar on observables but have discordant treatment status.

> 20 In Appendix Table 4, we present baseline balance of covariates by quintiles of the propensity score, which is recommended as a test of balance in propensity score matching (Imbens 2004).

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analysis employed in this paper could shed some light on the role of a broader country context in the interest rate pass-through.

The global financial crisis marked a significant shift in the pass-through (Hristov et al., 2014; Aristei and Gallo, 2014; Gambacorta et al., 2015). The interest rate transmission lost much of its strength and the pass-through became more uniform across different lending rates. One explanation for the weakened transmission could be a greater sensitivity to risks and uncertainty in the post-crisis era. However, some studies such as von Borstel et al. (2016) argue that the interest rate pass-through remained strong and mostly complete even after the global financial crisis. They see the difference in the post-crisis transmission in the composition of the pass-through, but not in its overall strength. In particular, they decompose the pass-through from the monetary policy rate to bank lending rates into four individual components. Accordingly, they find that while the overall pass-through has remained unchanged, the individual components such as the term spread, sovereign risk, banks risk, and credit risk have seen significant but offsetting shifts.

# **3. Method of Paper Selection**

To select papers for our meta-analysis, we used the following search approach: First, we created a database of all studies focusing on the interest rate transmission. For the search, we used Google Scholar, Scopus, and the Web of Science. The key words entered in each database were “Interest Rate PassThrough” and “Interest Rate Transmission.” After the primary search, we also performed an alternative search for any other relevant studies cited in the papers retrieved in the first round. The search was completed by mid-2017. Therefore, studies published after July 2017 are not considered. The conducted search produced a data set of over 100 studies.

Not all the 100 studies, however, met our three selection criteria. First, we selected only studies published in peer-reviewed journals, and working, discussion, and research paper series registered in the REPEC database. Further, we selected only studies that estimated the coefficient of long-run interest rate pass-through. This step substantially reduced our data set. Third, we selected papers that publish 7
The coefficients on population size and school enrollment are positive and significant in the AKI regression. This indicator is higher in the United States and Canada than in countries in other regions. The AKI regression has less explanatory power than the SPI, MSC, and CS regressions, with an adjusted _R_<sup>2</sup> of only 0.445.

The DPO regression shows the positive impact of economic complexity and the level of education. Unlike other regressions and the findings by ODW (2020), the DPO component is negatively correlated with the log of per capita GDP. The regional effects are consistent with regression results for other components; The NSSs of the United States and Canada have better dissemination practices and greater openness than the statistical agencies of other regions.

Figure 4 shows the scatter plot of the actual and predicted values of SPI (based on the regression in Table 2). The predicted SPI reflects the potential equilibrium supply of statistical information that a country should have, given its characteristics. Because at equilibrium, the supply of data should correspond to the demand for data, we interpret the difference between the observed and predicted SPIs as the difference between the supply of and demand for data.

The regression of the SPI has strong explanatory power, which is evident from the tight distribution of countries around the 45-degree line. That line separates countries into two zones. In countries above the 45-degree line, the predicted SPI is larger than the actual SPI. Given these countries’ characteristics, their NSSs undersupply data to data consumers, both domestic and international. The NSSs of countries below the 45-degree line oversupply data, given the profiles and characteristics of their data users.

Figure 5 presents a cut-out of the rectangle in Figure 4. The SPI of the NSO of one of the rich countries (ORC) is comparable (actual SPI = 0.43) to the SPI of Nicaragua (NIC), a country with a per capita GDP at least 15 times lower (about $1,700). The economic complexity, GDP per capita, and level of education in this rich country indicate that the demand for data there exceeds the supply and that its NSO should perform at the level of Oman (predicted SPI = 0.50). The SPI of the National Institute of Statistics of Guatemala is similar to that of the NSO in El Salvador (actual SPI = 0.57). The predicted SPI of 0.51 places Guatemala’s NSO close to the NSO of Bangladesh.

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-2-

This paper uses the classical Solow model as a general guide. The Solow model considers an efficiency parameter in the aggregate production function. Most cross-sectional studies of growth and convergence (including Mankiw, et. al.) identify the efficiency parameter with the constant in their regression equations. In so doing, these studies assume that aU countries have the same level of efficiency in using the factors of production. If we consider that the efficiency parameter depends on elements such as fiscal and taxation policies, openness to trade, public infrastructure, political stability, and level of education, we cannot but reject the assumption that all countries have a common efficiency parameter. In this paper, we deal explicitly with the issue of different efficiency parameters by identifying them with county-specific factors, which can be accounted for by using panel data. In principle, we would like to obtain information about all those variables that constitute the efficiency parameter. Unfortunately, adequate information is unavailable for most of those variables. Chamberlain (1984) shows a method to avoid the omitted-variable bias that occurs when all or some of the elements of such country-specific factors are unavailable. We will use Chamberlain's proposed methodology. In the estimation section of the paper, we consider first the case in which no information as to the country-specific factors is available, and second, the case in which we have information concerning one of its elements, namely, the country's level of education.
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**19. Lending cuts by European banks focused primarily on dollar-denominated loans and loans with higher risk weights, as suggested by March 2012 data from the Bank of International Settlements (BIS).** In particular, European banks reduced funding contributions to new syndicated, bilateral leveraged- and project-finance loans between the third and fourth quarter of 2011. Banks with EBA capital shortfalls reduced their lending sharply for all categories—especially leveraged loans, aircraft/ship leasing, and project and trade finance (figure 14). Notably, increased financing from other banks, asset managers, and bond market investors largely compensated for the cuts by European banks in the third quarter of 2011, leaving the overall volume of new syndicated and large bilateral loans essentially the same as in the third quarter of 2011. Trade financing, in particular, seems to have been picked up by Asiabased and other lenders, helping to limit its overall decline.

<!-- Start of picture text --> Figure 13. Potential Impact of Deleveraging on Figure 14. Changes in New Lending by Type of<br>Trade Finance Lender and Loan<br>Top 15 banks by market share in trade finance Change in new lending<br>All loans<br>MIZ (%) between Q3 2011 and Q4<br>2011, in %<br>TAN<br>BBVA $ denominated All lenders world-wide<br>MUFG<br>Other EU lenders<br>SMFG<br>SAN Trade finance Weaker EU banks 1<br>ING<br>DBK<br>RBS Project Finance<br>HSBC<br>CASA<br>CDB Aircraft/ship leasing<br>Soc Gen<br>Citi<br>BNP Leveraged<br>0 2 4 6 8 10 12<br>5 -5 -15 -25 -35 -45<br><!-- End of picture text --> Source: Panel a, Citigroup, Barclays, Deutsche Bank; Panel b, BIS _Quarterly Review_ , March 2012.

# **20. Additional indications provide some cautious optimism that a disorderly process of massive global deleveraging may have been avoided, at least in the short term:**

- Capitalization plans submitted by European banks to the EBA in January 2012 suggest that banks intend to meet the 9 percent core Tier 1 capital target primarily through direct capital impact measures (new capital and reserves, conversion of hybrids, issue of convertible bonds, retained earnings, and the like), and that the shedding of assets will play a small role. More specifically, the plans suggest that 23 percent of the recapitalization amount will be met through RWA reductions: 10 percent of this  is expected to come from asset shedding—sale of assets (65 percent), cuts in loan portfolios
factors limiting TFP growth have been the dominance of commodity production sectors, inefficient investment, weak competition due to the large role of the state, and armed conflicts.

In the past decade, the contribution of labor force growth to potential output growth has declined mainly because of a precipitous slowdown in population growth, particularly in the member countries of the Gulf Cooperation Council (GCC). Labor force participation rates also declined, particularly among oil importers. The contribution of labor force growth to potential growth was also held back by the region’s female labor force participation rates, which are among the lowest in the world. For example, women make up just under four-tenths, on average, of the populations of GCC economies and yet represent only about one-tenth of the labor force. Moreover, while educational attainment among both men and women improved in the past decade, the quality of education, as measured, for example, by primary school proficiency tests, remained lower than in most other regions (World Bank 2018b).

The pandemic did further damage to the drivers of potential growth. Fixed investment in 2021 was more than 10 percent lower than was expected prior to the pandemic, with negative and long-lasting consequences for the growth of the capital stock. Human capital has also been eroded by higher long-term unemployment, disruptions to education, and a deterioration of health outcomes. Pandemic-related school closures since 2020 have averaged 48 weeks in MNA, above the global average of 38 weeks. This outsized damage to human capital accumulation is likely to have undermined poverty reduction and impaired the lifetime earnings of many (Azevedo, Hasan, et al. 2021).

# 8.3. Potential growth prospects in MNA

Over the 2020s, potential output growth in MNA is expected to remain weak, at 2.5 percent a year, only marginally above its 2010s average annual rate of 2.4 percent. Per capita potential growth is expected to increase to 1.3 percent from 0.8 percent in the 2010s. This mainly reflects a tepid improvement in TFP growth, which is expected to offset a further projected decline in the contribution of labor force growth, in part due to projected changes in demographic structures. Population growth is expected to slow to 1.3 percent a year on average, down from growth of close to 3 percent a year on average in the two decades before the pandemic. The working-age share of the population is expected to rebound to its 2013 peak, after a decade of decline.

The outlook for potential growth is underpinned by recent progress in structural reforms, particularly in the GCC economies. These include the increased participation of women in the workforce, improvements in the business climate, and diversification of the economies of commodity-dependent countries. Outside the GCC economies, however, reform momentum has remained lackluster.

In Saudi Arabia, increasing female labor force participation and reforms to the Kafala sponsorship program for expatriate workers have created a strong foundation for improving potential productivity growth, particularly by improving skill matchings and