rafmacalaba/datause-encoder-data huggingface dataset:
- 11 -

concentrations of fairly srnall particles, but they do not measure particle mass like TSP and PM10. However, available data for co-located BS and TSP monitors in London indicate an average ratio of BSITSP of 0.55, the same as the ratio of PM1O to TSP." Based on this and additional analysis by the California Air Resources Board." it is assumed that PMIO is roughly equivalent to British smoke.

Premature Mortality. Since the effects of particulate air pollution on mortality is the source of such large potential benefits, the evidence for an effect and its potential magnitude will be reviewed in detail The most relevant studies are reviewed below.

_London._ Among the earliest empirical estimate of mortality outcomes associared with particulate mater is the analysis of data from London for the winter of 1958-59, where a stistically significant relationship was found between daily deaths and daily levels (24-hour average) of British smoke. London data for 14 winters, 1958-59 through 1971-72 have been analyzed by Mazurmdar et aL,'<sup>3</sup> Ostro 1 and Schwar and Marcus,'S'<sup>6</sup> and reviewed by the U.S. EPA.'7 En the earlier winters, the levels of British smoke were extremely high; the mean for the first seven winters was 270 _psglm_<sup>_3_</sup> _._ However, the mean for the last seven winters was 80 _gg/m3 ,_ and in three of the last four years the mean concentrations were below 70 gg/m<sup>3</sup> . The concentrations of British smoke in London in the last years are more comparable to those commondy found in the U.SAlthough these analyses involve several different statistical methds, the following general conclusions can be drawn: (1) there is a strong relationship between particulate concentatons and daily mortality in London, which holds both for the entire data set and for individual years (the larer years exhibited almost an order of magnitude decrease in air poliution concenrations); (2) there is no indication of a "no effects level' (i.e-, a threshold) at the lower concentrations of air pollution experienced in London; (3) the association between air pollution and mortality cannot be 'explained away" by meteorologic factors or by serial correlation in the data; and (4) regardless of the model specified, the quantitative implications of the studies are very simfilar. For this review, quantitative estmates of the
Table 12

||Outstand|ing Loan<br>(bil|s to Pers<br>lion yen)|onal Sect|or|
|---|---|---|---|---|---|
||1965|1970|1975|1980|1985|
|Housing Loans|740|4365|21850|44313|68532|
|GFIs|286|886|3476|||
|PFIs|92|1858|12669|||
|Employer Loans|362|1621|5705|||
|Consumer Loans|563|1705|4083|15516|27395|
|Loans|103|584|1407|9161|16698|
|Finance Co.|1|5|215|1705|1925|
|PFIs|50|520|1054|6818|12401|
|Pawn Shop<br>Post Office|52|59|68<br>70|||
|Sales Credit|460|1121|2676|6356|10697|
|GNP|32813|73046|151797|245163|325371|

Housing+Consumer/GNP 4.0% 8.3% 17.1% 24.4% 29.5% Housing+Consumer/TOTAL loans and discounts (Table 6) 3.2% 6.7% 11.7% 15.5% 15.9% Source: _Shohisha Shlnyou Toukel_ (Consumer Credit Statistics), 1978 and 1988 and Bank of Japan, Economic _Statl8tical_ Annual, various i88ues.

43
data for other non-bank financial service providers (such as credit unions, cooperative, finance companies, and microfinance institutions), which are often not regulated.

Using data gathered through our survey of bank regulatory bodies and from other sources, we put together the following indicators of banking sector outreach:<sup>7</sup>

- 1- Geographic branch penetration: number of bank branches per 1,000 km<sup>2</sup>

- 2- Demographic branch penetration: number of bank branches per 100,000 people

- 3- Geographic ATM penetration: number of bank ATMs per 1,000 km<sup>2</sup>

- 4- Demographic ATM penetration: number of bank ATMs per 100,000 people

- 5- Loan accounts per capita: number of loans per 1,000 people

- 6- Loan-income ratio: average size of loans to GDP per capita

- 7- Deposit accounts per capita: number of deposits per 1,000 people

- 8- Deposit-income ratio: average size of deposits to GDP per capita

Indicators (1) through (4) measure the outreach of the financial sector in terms of access to banks’ physical outlets. The data for each of these indicators, across 98 countries in the case of branches and 89 countries in the case of ATMs, are shown in Table I. The indicators of branches and ATMs per square kilometers help characterize the geographic penetration of the banking sector. They can be also interpreted as proxies for the average distance of a potential customer from the nearest physical bank outlet. Higher geographic penetration would thus indicate smaller distance and thus easier geographic access. Per capita measures of branches and ATMs are used to capture the demographic penetration of the banking sector. They proxy for

> 7 In previous versions of the paper, we reported combined indicators, such as principal component indicators combining the geographic and demographic penetration of branches or ATMs and residuals of a regressions of branches/ ATMs on area and population.  However, unlike the indicators presented here, they are hard to interpret and imply certain assumptions about the importance of each dimension of outreach.

8
**Figure 4. Quantitative easing and central banks’ balance sheets**

**A. Size of asset purchase programs**

# **B. Share of government debt held by central banks**

<!-- Start of picture text --> Percent of GDP Percent of debt outstanding<br>100 40 Bank of Japan<br>Announced<br>U.S. Federal Reserve<br>80 Realized 30 European Central Bank<br>60<br>20<br>40<br>20<br>10<br>0<br>United States Euro Area (Mar Japan (Apr 13 - 0<br>(Dec 08 - Oct 15 - Mar 17) Dec 17) 2010 2012 2014 2016<br>14)<br><!-- End of picture text --> Sources: J.P. Morgan, Bloomberg, Federal Reserve Bank of St. Louis, Haver Analytics, National central banks, World Bank. A. Net asset purchases since the beginning of quantitative easing programs. Announced estimates based on stated policy intentions as of May 2016. The ECB’s QE program is assumed to end in March of 2017 and the Bank of Japan’s QQE is assumed to continue until the end of 2017.

B. Last observation is 2016Q1 for the U.S. Federal Reserve and the European Central Bank, and 2015Q4 for the Bank of Japan.

programs, as well as exchange rate interventions. As a result, their balance sheets have expanded rapidly (Figure 4). In some cases, central bank purchases have drawn down the pool of eligible assets. For instance, asset purchases by the ECB, which amount to €80 billion per month until at least March 2017, mainly comprise sovereign bonds based on a defined allocation key and strict eligibility criteria. Specifically, these criteria prohibit purchases beyond 25 percent of the outstanding amount of individual securities, 33 percent of any given issuer’s debt, and of bonds with yields below the ECB’s deposit rate, currently set at –40 basis points. Despite a relatively limited fraction of government debt currently held by the ECB, scarcity considerations could intensify given the increasing share of highly rated sovereign bonds trading at yields closer to the lower limit of eligibility.<sup>8</sup> The risk of mismatch between the effective supply and the intended scale of ECB’s government bond purchases encouraged a broadening of eligibility criteria and the implementation of other easing measures, including purchases of corporate sector bonds and NIRP.

In Japan, the size of central bank balance sheet and the share of government debt held by the BoJ increased sharply under its Qualitative and Quantitative Easing (QQE) program, initially launched in April 2013. Under the current program, government bond purchases will cover roughly half of newly issued debt expected for fiscal year 2016 (Japan Ministry of Finance 2016; BoJ 2016b). The BoJ committed to purchase other types of assets, such as exchange-traded funds, real estate investment trusts, and corporate bonds, before deciding to introduce NIRP to signal a further extension of policy easing measures.

> 8 IMF (2015a) argues that the ECB could preempt the risk of shortage in these bonds by having a more flexible menu of assets to purchase. The ECB started buying corporate bonds in June 2016.

13
largely due to bank restructuring that took place after the East Asian financial crisis of 1997.

# 4. Safety Net Subsidies

We have calculated annual gross safety net subsidies as one-year put options on the value of bank assets for the 137 banks for each of the years between 1991-98 using Ronn and Verma's (1986) method. For all the countries in our sample we find that the gross safety net subsidy is increasing over the time period from an average of 2 basis points (bp) per annum in 1991 to 216 bp per annum in 1998, and, more specifically, that the average subsidy is higher during the crisis period 1997-98 than during the pre-crisis years (see Appendix, Table 3.a).

Over the sample period, the gross safety net subsidy (averaged across all banks in the country and over all years) is highest for the four East Asian crisis countries Indonesia (154 bp), Thailand (136 bp), Korea (37 bp) and Malaysia (26 bp), and for Hong Kong (38 bp). The high subsidy of Hong Kong might be explained by contagion effects. The subsidy (averaged across all banks in the country and over all years) is lowest for the four highly developed Western countries Germany (0.18 bp), US (0.4 bp), UK (1.3 bp), and France (2.4 bp), and for Taiwan (1.3 bp). Taiwan's financial system is predominantly state-owned and banks are highly protected, which might be an explanation for our result that suggests that Taiwanese banks take low risks. The calculated subsidies of the remaining Asian countries Singapore (6 bp) and Japan (12 bp) are somewhere in the middle (see Appendix, Table 3.b, for further details).

The subsidies indicate that risk-taking is different among ownership forms. The gross subsidies across the different ownership forms are (in decreasing order of risk and due to merger activity. Commercial Bank of Korea and Hanil Bank merged in 1998 creating a new bank called Hanvit Bank, while Hana Bank announced on September 8'h, 1998 to merge with Boram Bank (to become effective in 1999). Korea First Bank has been sold to New Bridge Capital of the US as of December 30e, 1998, although trading was not suspended until June 25 , 1999, and Kookmin Bank announced a merger with Korea Long Term Credit Bank on August 25"', 1998. Accounting data for Seoul Bank continued to be reported until 1998, although the bank was nationalized in 1998, and subsequently sold to HSBC Bank on February 22nd, 1999. In Thailand, due to lack of data, we exclude Laem Thong Bank, Nakornthon Bank, and Union Bank of Bangkok. For 1998, we also do not have data on Bangkok Bank of Commerce, which was closed and de-listed in 1998, and on First Bangkok City Bank, which was acquired by the government in February of 1998 and merged with state-owned Krung Thai Bank in 1999.

13
prots net of capital adjustment costs. Investment becomes productive with a one period lag. We adopt the specication of Bloom (2009) and Cooper and Haltiwanger (2006), with tree types of costs of adjustment of the capital stock: xed adjustment costs, quadratic adjustment costs, and partial investment irreversibilities. The investment cost function is where **1** [ _Iijt_ = 0] , **1** [ _Iijt >_ 0] and **1** [ _Iijt <_ 0] are indicator variables that are equal to one when investment is non-zero, strictly positive, and strictly negative, respectively.

The rst term captures xed adjustment costs, which are paid whenever investment or disinvestment take place. Fixed costs are independent of the investment level in order to capture nonconvexities and increasing returns to the installation of new capital.<sup>5</sup> The second term captures the quadratic adjustment costs. These are variable costs that increase with the level of the investment rate (Dixit and Pindyck, 1994). Finally, the last two terms in (4) capture partial irreversibilities related to transactions costs, reselling costs, capital specicity and asymmetric information (as in the market for lemons). These costs are incorporated into the model by assuming a gap between the buying price _pb_ (1 + _τK_ ) , which includes the tari on imported capital goods _τK_ , and selling price _ps_ of capital so that _pb > ps_ .

The presence of xed costs and irreversibilities generates a region of inaction for the rm, as well as regions of investment and disinvestment bursts. Following a negative shock rms may hold on to capital in order to avoid xed costs and reselling losses; conversely, in periods of high protability, rms may choose not to increase the capital stock as much, in anticipation of eventual future costs of selling that capital, or not at all, to avoid xed costs. Quadratic adjustment costs, on the other hand, create incentives to smooth out investment over time. The patterns of capital adjustment in turn aect next period's labor demand.

> 5We assume that these costs are proportional to the pre-existing stock of capital _Kijt_ at the rm level. Proportionality with respect to _K_ captures the fact that as a rm grows larger xed costs of investment do not become irrelevant, and, on the contrary, the importance of indivisibilities, plant restructuring, worker retraining and production interruption, increase with rm size. Fixed costs can be modeled as proportional to the level of sales or prots at the plant-level; see for example Bloom (2009), Cooper and Haltiwanger (2006), Caballero and Engel (1999). Alternatively xed costs can also be modeled as independent of rm size, as in Rho and Rodrigue (2016).

9
# **I. INTRODUCTION**

Consider the evolution of GDP per capita and inequality in per capita consumption in Poland and Russia, the two largest transition economies of Eastern Europe and the former Soviet Union respectively. Poland, shown in the left panel of Figure 1 experienced a relatively shallow transitional recession and a decline in inequality, after which there was a more gradual increase in inequality with some temporary reversals, a pattern which exemplifies developments in Central Europe more generally. This was however followed by a sharper increase during the late 1990s and early 2000s to the point where the Gini coefficient of inequality was more than 25 percent higher in 2003 compared to 1989. In contrast, Russia, shown in the right panel of Figure 1, which broadly exemplifies developments in the CIS countries, experienced a wrenching transitional recession accompanied by an explosive increase in inequality which peaked in the mid-1990s. However, this was moderated to some extent during the very rapid growth that occurred after the 1998 financial crisis, so that the Gini coefficient was 10 to 15 percent higher in 2003 compared to 1991. Indeed, since 1999, the transition economies of the former Soviet Union have grown at rates approximating China’s extraordinary performance and, together with the transition economies of Eastern Europe, surpassed the pre-transition levels of GDP per capita for the region in 2004. While these developments are encouraging, they have occurred in the shadow of the realization that rapid growth in China, shown in Figure 2, has been accompanied by a steep increase in the Gini coefficient of income inequality by 2 percentage points a year between 1990 and 2001, to the point where the Gini coefficient was nearly 50 percent higher in 2003 compared to 1981. For countries in Eastern Europe and the former Soviet Union which share a socialist legacy with China, this could be seen as a harbinger of things to come.

**Figure 1  Poland and Russia: Real per capita GDP and Gini index, 1990-2003 Poland Russia**

**Russia**

<!-- Start of picture text --> Real GDP Per Capita Gini index GDP Per Capita Gini Index<br>175 175<br>150<br>150<br>125<br>125<br>100<br>100<br>75<br>75 50<br>1985 1987 1989 1991 1993 1995 1997 1999 2001 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Index, 1981=100 Index, 1990=100<br><!-- End of picture text --> Source: For Poland: Keane and Prasad (2002a) for 1985-1997 (Gini for consumption per capita without durables), own estimates for Gini based on regional data archive, Russia: simulations based on published expenditure distributions for 1990-1996 and own estimates for consumption Gini based on regional data archive.

4
Policy Research Working Paper 10349

# **Abstract**

This paper studies the effect of proposing a monetary incentive for vaccination intentions, with a survey-based randomized controlled experiment conducted separately in three countries, Tajikistan, Uzbekistan, and Kazakhstan. Respondents from nationally representative surveys were randomly assigned to a control group (for which no incentive was proposed) or to one of several treatment groups with varying levels of hypothetical compensation. Offering incentives markedly reduced overall vaccination intentions —all three counties. Country-level results ranged from no meaningful effect on vaccination intentions (Tajikistan) to a decline of up to 22 percent (Uzbekistan and Kazakhstan).

In follow-up questions, most respondents said they disapprove of offering financial incentives for vaccination, and especially in contexts with strong negative effects in the experiment. The results contrast with the well-established efficacy of monetary incentives to influence vaccination behavior in other settings, but they are consistent with findings from the behavioral literature in which incentive payments signal inferiority or disutility. The findings suggest that policy makers and practitioners should use caution when considering extrinsic incentives for vaccination and other health interventions where effects have not been tested.

This paper is a product of the Poverty and Equity Global Practice. 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 wseitz@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 Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Figure 1: Chile Insurance Premium: 1990-2008**

(as a percentage of GDP) <!-- Start of picture text --> 4.5<br>4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br>Life Premia Annuities Premia Total Premia<br><!-- End of picture text --> Source: SVS The expansion of the insurance sector is also reflected in its total assets, which grew from about 5 percent of GDP in the mid-1980s to about 20 percent of GDP in 2003. With the exception of 2008, where figures are volatile due to the effect of the financial crisis, the ratio of total assets of insurance companies to GDP has remained relatively stable since 2003. As shown in Figure 2, the assets of the insurance sector are still smaller than the assets of the pension sector. The increase in the ratio of insurance assets to pension assets between 1990 and 2003 was followed by decreases in this ratio to a level similar to that prevailing in 2009. This declining trend is partially explained by regulatory changes that tightened the requirements for early retirement.<sup>6</sup>

**Figure 2: Pension and Insurance Assets 1990-2009** (as a percentage  of GDP)

<!-- Start of picture text --> 70 40<br>60 35<br>30<br>50<br>25<br>40<br>20<br>30<br>15<br>20<br>10<br>10 5<br>0 0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br>pension funds insurance companies Insurance/Pension Assets<br>Pension Insurance Assets Ration Insurance/Pensions<br><!-- End of picture text --> Sources: SVS, SAFP 6The true ratio is higher than 28 percent, because insurance assets are measured by a combination of market and book values, while pension fund assets are measured at market values. Due to decreases in the value of pension fund assets and a steady increase in the value of insurance assets, the ratio in 2008 increased to 38 percent.

4
# **3.3.Validation of the Asset Index**

We examine to what extent the asset index overlaps with other indices, i.e. the extent to which one acts as an indicator for the other. One possible way of examining this is to define a poverty threshold for predicted income and one for assets. The proportion of people classified as poor under both thresholds can then be examined and compared with those classified as poor under only one threshold and with those not classified as poor under either threshold. However, the results yielded may be sensitive to the threshold that was selected. Alternatively, the receiver operating characteristic (ROC) curve provides a useful procedure for this comparison. It is arguable that the area under the ROC curve gives a more intuitive summary of the extent to which two dimensions of welfare are correlated in the sense of identifying the poor. Figure 1 suggests that asset-based poverty is a good indicator of income-poverty (when using predicted income as a proxy for well-being). With an area below the ROC curve of around 0.85, this suggests that targeting low-asset households is going to alleviate much of (though not all) poverty as measured with the predicted income, and vice-versa.

The ROC curve methodology states as follows. Let us consider income-poor households that are below a certain threshold (that is, US$2 when considering poverty and US$1 when considering extreme poverty). If the asset index assigns someone as poor who is also poor under the income-poverty definition then this is called a ―true positive‖ (TP), also called ―sensitivity.‖ If it signals as poor someone who is not poor under the income definition, it is a ―false positive‖ (FP), also called ―(1 – specificity),‖ which is also known as a type I error (i.e. poor people classified as non-poor). If it signals someone as non-poor even though this person is poor under the income definition, it is a ―false negative‖ (FN). Finally ―true negatives‖ (TN) are those who are classified as non-poor under both definitions.

**Figure 1. Asset-based poverty and predicted income-poverty**

<!-- Start of picture text --> 0.00 0.25 0.50 0.75 1.00<br>1 - Specificity<br>Area under ROC curve = 0.8535<br>1.00<br>0.75<br>0.50<br>0.25<br>0.00<br><!-- End of picture text --> Source: Own computations using HLCS 2001 and DHS 2005 Table 2 summarizes the results together with Spearman rank correlations between HAZ-score and alternative measures of well-being. As for the area under the ROC curve, it is difficult to settle, from this analysis, on which of these two indices is the best predictor 9