rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 3. 9  Banked versus unbanked users of informal savings services**

<!-- Start of picture text --> 100%<br>90%<br>80% 42.7%<br>70% 63.6%<br>60%<br>50%<br>40%<br>30% 57.3%<br>20% 36.4%<br>10%<br>0%<br>Used informal savings during the last Did not use informal savings during the<br>12 months last 12 months<br>Banked Unbanked<br>%<br><!-- End of picture text --> _Source: DANE household study of Bogotá, 2003_

# **3.4 CREDIT SERVICES FOR THE UNBANKED**

22. **Informal credit is used by both the banked and unbanked.** 14% of the survey population uses informal credit services. In line with the finding on informal savings services, demand for informal credit is higher from banked than from unbanked individuals. 58% of informal credit users are banked, while 42% are unbanked. This finding could be partly explained by the fact that the among the unbanked only 9% applied for an informal loan during the past three years, while 20.2% of the banked did, increasing the likelihood of the banked population over the unbanked to obtain informal credit.

**Table 3. 3  Banked versus unbanked users of informal credit services**

||**Banked**|**%**|**Unbanked**|**%**|**Total **|**%**|
|---|---|---|---|---|---|---|
|Uses informal credit|339,061|7.9%|244,300|
> ). However, by operating in the shadow economy, informal businesses are likely cut off from the formal financial system as they lack the legal basis that may be necessary to access it directly. Consequently, informal businesses likely have to rely on informal sources of credit, or access finance through credits to individuals or households. Indeed, one of the oftentouted benefits of formalization is that it improves access to formal credit, thus enabling businesses to seize growth opportunities. The <mark>Informal Sector Enterprise Surveys collect data on a variety of variables related to the informal sector’s access to financial products. Overall, we find that, as expected, informal businesses are poorly integrated into the formal financial system, reliant mostly on the owner’s personal funds and on informal sources of credit. Most businesses were started with the owner’s own funds: over 95% in Lao PDR, 81% in India, 84% in Iraq, and close to threequarters of businesses in Somalia, Mozambique, and Zambia. The next most common source of</mark> 15
is not sustainable, however, since mean-zero stationary of q,,iB,,i is only _a sufficient,_ 6 Using data for eighteen OECD countries, Rose's (1988) econometric examination of nominal interest rates and inflation rates implies that _real_ interest rate are, in fact, nonstationary. Even if the real rate was stationary, the assumption of a constant expected rate implies the absence of serial correlation in the series, which is clearly contradicts the facts for U.S. T-bill rates.

27
to reducing country risk. Also, a reduction of government expenditures, for example, through privatization of SOEs or reduction in bloated bureaucracies, would help to (a) increase the rate of income growth (allocation effect) and (b) reduce the interest rate (crowding out effect).

# **Description of Some Debt Statistics for Selected Countries**

Tlhe remaining sections will present some variables relevant to the debt sustainability of Brazil, Chile and Costa Rica, with passing references to Germany and Argentina.

# **Brazil**

Brazil is an interesting country given its broad experience with inflation, debt problems and extensive debt-financing strategies. In the previous debt sustainability exercise, we have demonstrated that k is a key variable. The following illustration will show the evolution of k for Brazil.

Chart 1 18
**Figure 4:  The impact of the crisis by region, gender, education and age group**

<!-- Start of picture text --> Poverty by region Poverty by Gender<br>Rîga Pierîga Vidzeme Kurzeme Zemgale Latgale Female Male<br>Pre-crisis Pov. Sim. Pov Pre-crisis Pov. Sim. Pov<br>40<br>30<br>20<br>10<br>0<br>20<br>15<br>10<br>poverty rate<br>5<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Poverty by Education (people aged more than 24) Poverty by age group<br>Unskilled Rest Children Rest<br>Pre-crisis Pov. Sim. Pov Pre-crisis Pov. Sim. Pov<br>20 40<br>15 30<br>10 20<br>poverty rate<br>5 10<br>0 0<br><!-- End of picture text --> Source: Authors calculations based on 2006 EU-SILC.

**Figure 5:  Growth incidence curve for Latvia: Percentage increase in per capita household income between 2008 and 2009**

<!-- Start of picture text --> 0 20 40 60 80 100<br>Percentile<br>Densely populated area Thinly populated area<br>All<br>0<br>-10<br>-20<br>-30<br><!-- End of picture text --> Source: Authors calculations based on 2006 EU-SILC.

11
# _d. How good are annual data for measuring educational performance?_

So far we have been using the data from the 2010 EIT. We also have data for the 2008 and 2009 EIT. Given that we have 3 years of data for both inputs and outputs one could argue we can use panel data techniques to estimate the determinants of educational performance in Ukraine. However, yearly test score data can be quite crude measures of educational performance. To illustrate this, we follow Kane and Staiger (2002) who suggest several ways to check to what extent test scores can be used as school level indicators of performance.

First, one can check to what extent scores are correlated over time – that is, to what extent do schools that score well in one year also score well in subsequent years. The idea behind this measure is that one reasonably can expect school quality to be, to a large extent, stable over time. If our school performance measure varies a lot from one year to another year, with changes in one year reverting the next year, our school performance measure is likely to be a noisy indicator of the underlying real school quality. We use the Ukrainian exam as this exam is compulsory for everybody who is interested in further studies, and we use the mean score by school as score indicator.

**_Figure 2: Correlation in Test Scores_**

<!-- Start of picture text --> Correlation in Test Scores over Time<br>90<br>80<br>70<br>60<br>50<br>40 Ukraine<br>US<br>30<br>20<br>10<br>0<br>0 1 2 3<br>Number of Years Apart<br>Correlation<br><!-- End of picture text --> 48
the earnings which include both measurement error and transitory fluctuations in earnings. Following Tansel and Bircan (2006), I use total family expenditure as a proxy for family income because total expenditure represents permanent income better than current income. Moreover, there are fewer errors in measuring total expenditure than in measuring income. I also use family income itself as a robustness check. I estimate the following equation: where _Edu_ expispt_ is household education spending of household _i_ in industry _s_ and province _p_ at time _t_ . � _ln_ ( _Total_ expipt_ ) (as a proxy for the family income) is the fitted value of total household expenditure derived from the the first stage equation given by: The vector _Xispt′_<sup>is a set of family specific characteristics that are correlated with both educational spending and</sup> income e.g., parents’ education. Since education spending and total household expenditure are both in logarithmic form, _ξ_ denotes elasticity.

Table 5 presents the maximum likelihood estimation results of Eq (4) and Eq (5) (the unconditional marginal effects). I find that income elasticity is significantly greater than one (3.284). Thus, as total expenditure decreases, education spending decreases more rapidly. The F-statistic in the corresponding first-stage regression is far above the critical value, indicating that the used instrument is relevant.

Table 5: Income Elasticity of Education Spending

|Dependent variable: Ln(Household Spending on Education)|
|---|
|2SLS<br>Robustness Check|
|Variables<br>IV: Ln(Expenditure)<br>IV: Ln(Income)|
|IV<br>3.284<sup>_∗∗_</sup><br>2.049<sup>_∗∗_</sup>|
|(1.374)<br>(0.850)|
|_First stage_|
|IV: Treat _×_ Post2007<br>-0.111<
|Dep. Var: Hesitancy|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Oxford stringency index terciles (ref. –<br>bottom tercile)|||||||
|Stringency (middle tercile)|||0.061|||0.148|
||||(0.073)|||(0.077)|
|Stringency (top tercile)|||-0.005|||0.028|
||||(0.060)|||(0.059)|
|WHO excess deaths due to COVID-19|||||||
|(ref.- bottom tercile)|||||||
|Excess deaths (middle tercile)||||-0.110*<br>(0.050)||-0.123<br>(0.080)|
|Excess deaths (top tercile)||||-0.059||0.045|
|||||(0.067)||(0.106)|
|Confidence in government index tercile|||||||
|(ref.- top tercile)|||||||
|Confidence in government|||||0.073<br>(0.054)|0.105<br>(0.064)|
|Confidence in government (top|||||0.016|0.055|
||||||(0.131)|(0.135)|
|Constant|0.282*|0.286*|0.265|0.395**
**_<u>________________________________________________________</u>_**

# _<u>Stat-USA</u>_

Comprises GLOBUS & NTDB, The International Trade Library, and State of the Nation. Produced by the U.S. Department of Commerce.

GLOBUS (Global Business Opportunities) offers daily trade leads from the U.S. Trade Opportunities Program and U.S. Department of Agriculture. It also offers daily procurement activity from the Defense Logistics Agency, the United Nations and the Commerce Business Daily leads.

NTDB provides access to Country Commercial Guides, Market Research Reports, and Best Market reports. It also provides U.S. import and export statistics and other information. The International Trade Library is a comprehensive collection of over 40,000 documents related to international trade. All are keyword and full-text searchable.

State of the Nation provides U.S. domestic financial and economic information from throughout the U.S. federal government and other related entities.

# _<u>WITS (World Integrated Trade Solution)</u>_

Commodities trade database designed by the World Bank. Contains trade statistics from the COMTRADE database made available by the United Nations Statistics Division. WITS also contains tariff rates and non-tariff barriers and provides import and export data for 110 countries. For many of the countries, data can go back to 1962 and may be available up to the current year. Annual international trade statistics are detailed by commodity and partner country. Allows data to be viewed by product classification, reporting country, direction of trade flow, product, or year. Users can create and save queries for multiple countries, products, years, and flows; check data availability by country and product classification; create country and product groups; calculate simple averages or weighted tariffs rates; calculate effects of tariff reductions using the simulation function; and extract data in Excel format.

# _<u>Global Monitoring Report 2004; Overall Trade Restrictiveness Index (OTRI)</u>_

Global
|114.75|
|Petroleum Refining and Products|<br>85.02|83.71|
|Tires and Inner Tubes|90.94|87.56|
|Cement|90.70|84.59|
|Bricks|91.16|82.39|
|Iron and Steel|95.67|87.23|
|Radio and TV Receivers|72.77|106.52|
|Motor Vehicles|67.81|87.71|

Source: Figures for the quantity adjustment are from US Department of Commerce, 1982 US Industrial Outlook; figures for unit value adjustment from 1977 Census of Manufactures, after quantity adjustment from 1977 to 1975, and Annual Survey of Manufactures 1975-1976.

Since we prefer to uvse the method which assumes the price relationships for covered output in the industry to be representative for the industry as a whole, we restate formulae (3.5a) and (3.5b), as a consequence of the US quantity ard unit value adjustments, as follows. In formula (3.7a) US gross quantities for 1977 are adjusted to 1975 using the factor "q" derived from the first column in table 3.6: In formula (3.7b) gross quantity of output in Brazil and Mexico is weighted at 1977 US unit values, so that we had to apply the term **"p"** in table 3.6 to convert the comparison to 1975 US unit values:
digitalization, wages, and employment, with a focus on the automation of tasks and job polarization. Section 4 reviews the evidence on digitalization and access to markets, with a focus on prices and financial inclusion. Section 5 reviews the evidence on digitalization and public finances, with a focus on government revenues and spending. Section 6 concludes.

# **2. Digitalization and productivity growth**

In the neoclassical growth model, the role of technological change is reflected in the growth of total factor productivity (TFP).<sup>4</sup> Technology use can also raise labor productivity to the extent it increases the accumulation of capital. Early endeavors to measure the economy-wide effect of ICT adoption on productivity used data for the United States and found little or no effect (Berndt and Morrison 1995; Jorgenson and Stiroh 1995; Loveman 1993; Oliner and Sichel 1994). These results underscored the “productivity paradox” that Robert Solow first described in the late 1980s. As the data revealed a clear improvement in U.S. productivity in the late 1990s, the narrative shifted. This is evident from the growth accounting literature and from econometric evidence at the industry and firm levels (Brynjolfsson and Saunders 2010; Draca, Sadun, and Van Reenen 2009). However, these studies neither examine the channels through which ICT use improved productivity nor consider the second wave of digitalization.

## **_2.1 Does digitalization enable better matching of supply and demand?_**

The spread of the internet and digital platforms has improved productivity by enabling more efficient matches between buyers and sellers, as well as between firms and workers. Productivity gains are reflected in lower prices in advanced economies for a variety of products sold online— including books, insurance, air travel, and automobiles (Brynjolfsson and Smith 2000; Brown and Goolsbee 2002; Orlov 2011; and Scott Morton, Zettelmeyer, and Silva-Risso 2001). There is evidence of productivity gains in matching service providers and job seekers too. Based on firmlevel
In these countries, we see a correspondingly large discontinuity in test scores. Figures 5.5 and 5.6 depict the results graphically and Table 5.1 quantifies them. The figures show a sharp break in test scores at the grade cutoff, both in the individual countries (for some subjects) and for the pooled sample. In the regressions, the OLS results show that the additional year of schooling around the cutoff is correlated with between 39 and 48 points in math and reading. When we control for selection using regression discontinuity and instrumental variable approaches, the coefficient shrinks as expected. Nevertheless, they suggest that an additional year of schooling increase scores by between 12 and 35 points, depending on the method and sample used.

**Figure 5.5: Test Scores by Birth Month:  Individual countries and subjects**

<!-- Start of picture text --> Republic of Korea<br>Month Month<br>Test Score<br>Test Score<br><!-- End of picture text --> Source: Authors’ analysis of PISA 2015 data.

32
when political competition is clientelist. The analysis below also uses these distinctions in the political value of different types of public spending to show that the robust negative correlation of vote buying exists specifically for health spending.

Detailed research on Early Childhood Development (ECD) using panel data from the Philippines provides some _prima facie_ evidence of weak accountability for improving the quality of service delivery for the poor. Researchers document strikingly low levels of health and development among poor Filipino children (Ghuman et al, 2005; Glewwe and King, 2001). Ghuman et al (2005) find that variation in several indicators of child health and development is strongly correlated with variation in the availability of health and ECD services in the village. This suggests scope for municipal allocation of resources to reach the poor more effectively with the basic health services that can improve their well-being.

_Salience of vote buying in political contestation_ Qualitative evidence exists for the Philippines describing the salience of vote buying as a widely accepted practice among the poor (Schaffer, 2005; 2007). Citizens belonging to lowincome groups tend to defend the practice against “upper class” views, describing vote buying as a form of political attention and responsiveness to the needs of the poor. Citizens targeted with vote buying do not think of it as “selling” their vote, but rather as some evidence of the good will and quality of candidates which they factor into their decision-making.<sup>19</sup> Perhaps because of this, the direct survey questions used in this study, on the practice of vote buying in municipalities, seem to have worked well in measuring variation: people are less shy or wary of responding in the affirmative.

Furthermore, the Philippines may provide an appropriate context in which vote buying is a viable electoral strategy, even with secret ballots, operating through traditional institutions of reciprocity ( _utang na loob_ ).<sup>20</sup> Local political operatives in municipal politics might be particularly well embedded in communities and be able to target vote-buying offers to voters
<br>late<br>afternoon<br>for<br>25%<br>of<br>outdoor workers|Shift<br>working<br>hours<br>towards early morning<br>and late afternoon for<br>50% of outdoor workers|Averted<br>labor<br>productivity loss<br>|
|**Cool**<br>**roofs**<br>**program**<br>**(homes/workplaces/public**<br>**buildings)**|Introduce cool roofs on<br>representing 12.5% of the<br>total roof area of the city|Introduce cool roofs on<br>representing 25% of the<br>total roof area of the city|Avoided cooling costs|

# **2. Methods**

# **2.1 Heat hazard for three Indian cities under climate scenarios up to 2050**

Heat hazard was modeled for Chennai, Surat and Lucknow using UrbClim, a computer-based framework for modeling climate variables and high spatial resolution (De Ridder et al. 2015). A detailed representation of each city’s surface including land cover, built-up and vegetated area, terrain, and human-generated heat were constructed using remote sensing datasets (see Supplementary Figures - Table 1); this information was combined with data on the historical and projected future climate from the ERA-5 and CMIP-6 datasets respectively. For future periods, urban growth was incorporated into the modeling through use of projected population extents corresponding to future Shared Socioeconomic Pathway (SSP) scenarios calculated in a separate study (Wang et al. 2022). Model runs were performed for a present-day reference period and two 4
10 percent level, respectively. Standard errors presented in parentheses. Dependent variable: percentage point change in the headcount poverty rate in country _i_ from year _t − n_ to _t_ , where the headcount poverty rate is calculated as the percent of the population under the $1.90 poverty line.

It is clear from looking at the _R_<sup>2</sup> for each specification, that Equation (10) and (13) (Columns 4 and 5, respectively) do a far superior job at explaining changes in poverty in our sample. Moreover, it appears that our log-normality assumption provides us with a very reasonable approximation to the arithmetic identity that exists between changes in 13
targeting (Weitz-Shapiro, 2012). In this case too, as in roads, vote buying tends to be positively associated with receiving assistance in times of need (reported in Khemani, 2013).

That is, vote buying is systematically negatively correlated with a particular type of service with the following characteristics—that which is the exclusive responsibility of municipal governments, that which is pro-poor (services which the rich don’t use), and that which is a relatively broad, untargeted service, not particularly amenable to narrow targeting to select citizens. The correlation holds for a wide variety of measures of health services, obtained from three different sources: village health records, municipal budgets, and household interviews.

# **6. Discussion: Implications for theory and development policy**

Although theories of clientelism and vote buying have generally suggested that the practice of targeting benefits to voters in direct exchange for political support is likely to have pernicious consequences, there has been little detailed analysis of the mechanisms for this. Indeed, in the context of the Philippines, Schaffer (2005) has suggested that vote buying might be reflective of the responsiveness of politicians to poor and vulnerable citizens in particular. Vote buying need not be manipulative, but rather a reflection of how demanding are voters, to which governments may respond along multiple dimensions, giving gifts at election time, and all kinds of services at other times. This paper’s contribution is empirical—providing the first robust evidence that vote buying is associated with lower investments in a particular type of public 22
financial distress in the near future in the U.S. There are plenty of economic dislocations, inflationary fears, and frothy financial markets that resulted from the crisis and some of the policies to deal with it, that have the potential to create near-term banking problems. Nonetheless, the facts that the year 2020 went by without any significant financial distress reflected in the regulatory accounting data in the U.S. banking industry, that the limited information on stock market values suggest that the industry is now more valuable than before the crisis, and that systemic risk is measured as lower than before the pandemic qualifies as a favorable “financial surprise” in our view.

After our presentation of the accounting data in Section 4.1, we provide a few additional statistics that do not depend on accounting data to bolster our claims in Section 4.2. We briefly offer some more sobering thoughts for banking conditions in the remainder of the world in Section 4.3, and provide some suggestions for future research in Section 4.4.

# **4.1 U.S. Banking Data from 2019:Q4 to 2020:Q4**

Table 1 provides quarter-by-quarter snapshots of the U.S. banking system from 2019:Q4 to 2020:Q4 from the regulatory Call Reports, showing several dimensions of bank performance. Thus, we cover the industry from the quarter before COVID-19 struck in the U.S. in 2019:Q4, to the quarter containing the economic peak in 2020:Q1, to the quarter with the recession trough of 2020:Q2, to the two subsequent quarters of recovery, 2020:Q3 and 2020:Q4.

For each quarter, we show summary statistics for eight ratios of various ratios to Gross Total Assets (GTA), expressed in percent terms.<sup>14</sup> These ratios cover dimensions of bank condition (capital and liquidity ratios, based on equity and cash, due from, and securities,

> 14 GTA equals total assets plus the