rafmacalaba/datause-encoder-data huggingface dataset:
BOX 2.3 TEXT REFERENCE FOR THIS BOX Utility payments mostly digital in Kenya—but mostly in cash in Nigeria In both Kenya and Nigeria about 30 percent of adults reported having paid utility bills in the past 12 months. But there are big differences in the payment methods used.

In Kenya most people make utility payments digitally. Among those who reported paying utility bills in the past year, 55 percent made the payments through a mobile phone—including 11 percent who also made payments from a financial institution account—while another 6 percent paid utility bills from a financial institution account only (figure B2.3.1). Of the 55 percent paying utility bills through a mobile phone, 49 percent did so from a mobile money account and 6 percent through an OTC transaction.

# **FIGURE B2.3.1**

**Utility payers and how they make payments in Kenya and Nigeria Adults paying utility bills in the past year, by method (as % of all adults), 2014**

<!-- Start of picture text --> 30<br>Using other method<br>In cash only<br>20 From a financial institution<br>account<br>Through both a mobile phone<br>10 and a financial institution account<br>Through a mobile phone<br>0<br>Kenya Nigeria<br><!-- End of picture text --> In Nigeria, by contrast, 80 percent of adults paying utility bills reported doing so only in cash. Only 15 percent reported making the payments directly from a financial institution account only, while another 1 percent reported making them both from a financial institution account and through a mobile phone—in all cases from a mobile money account.

<!-- Start of picture text --> Source: Global Findex database.<br><!-- End of picture text -->

> THE GLOBAL FINDEX DATABASE 4141
Table 15: Linear probability model Innovation on IT use and competition changes

|Dependent variable: =1 if had innovation|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Innovation in product and processes||||||
|∆Computers-per-worker|-0.236**|-0.238**|-0.159|-0.164|-0.167|
||(0.102)|(0.101)|(0.108)|(0.106)|(0.107)|
|∆Chinese competition|-0.002|0.023|0.053|0.064|0.054|
||(0.175)|(0.176)|(0.174)|(0.174)|(0.174)|
|∆Computers-per-worker*∆Chinese competition|1.185**|1.197**|1.098**|1.107**|1.114**|
||(0.477)|(0.470)|(0.482)|(0.478)|(0.477)|
|Innovation in marketing and organization||||||
|∆Computers-per-worker|-0.191*|-0.195*|-0.106|-0.109|-0.112|
||(0.103)|(0.102)|(0.109)|(0.107)|(0.108)|
|∆Chinese competition|-0.116|-0.099|-0.067|-0.052|-0.062|
||(0.175)|(0.176)|(0.174)|(0.174)|(0.174)|
|∆Computers-per-worker*∆Chinese competition|1.121**|1.144**|1.034**|1.039**|1.046**|
||(0.475)|(0.472)|(0.484)|(0.480)|(0.480)|
|Controls||||||
|Age|No|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|
|FDI share|No|No|No|No|Yes|
|Observations|701|701|701|701|701|

Robust standard errors in parentheses Source: Authors’ calculations using data from ENTIC, INEGI

* _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01

39
# **Table 3: Panel B – Multinomial Regressions**

Log Odds ratio between Outright Owners and Mortgage holders (Marginal Effects)

|**Country**|**lage**|**female**|**hhsize**|**lnincome**|**hhnodep**|**hhdep**|**house**|**secedu**|**teredu**|**fulltime**|
|---|---|---|---|---|---|---|---|---|---|---|
|_New EU Countries_|||||||||||
|Czech Republic|0.1973 ***|-0.0101|0.0453 ***|-0.0152|0.0290|-0.1214 ***|0.7002 ***||0.4479|0.0014|
|Estonia|0.3668 ***|-0.0163|0.0565 ***|-0.0212|0.1266 ***|-0.0044|-0.0520 ***||-0.2967|-0.0175|
|Hungary|0.2588 ***|0.0099|0.0022|0.0244|0.0460 **|-0.0473 *|0.1333 ***|0.0637|0.1215|0.0217|
|Latvia|0.0808|0.0232|0.0007|-0.1522 *|0.0820 ***|0.0333|0.2193 ***|-0.0077|-0.0752|0.0119|
|Lithuania|0.2215 ***|0.0155|0.0202 **|0.0294 **|0.0346 **|-0.0290|-0.0084||-0.1828|-0.0205|
|Poland|0.3170 ***|-0.0120|0.0176 ***|0.0654 **|-0.0281|-0.0643 **|0.5769 ***|0.6416 ***|0.5662 ***|-0.0145|
|Slovak Republic|0.3462|-0.0218|0.0417 ***|-0.2012|0.0191|-0.1053 ***|0.6178 ***|-0.2883|-0.2234|0.0379|
|Slovenia|0.1664|0.0298 ***|0.0259 ***|0.0817 **|0.0538 ***|-0.1183 ***|0.1167 ***|-0.9303 ***|-0.9081 ***|-0.1548 ***|
|_EU-15 and Cyprus_|||||||||||
|Austria|0.7034 *|-0.0046|0.0376 ***|0.0035|0.0199|-0.1222 ***|0.3340 ***|0.4981|0.0671|-0.1241 ***|
|Belgium|0.5650 ***|-0.0239|0.0119|-0.0469|0.1614 ***|-0.1001 **|0.2518 ***|-0.9115 ***|-0.9582 ***|-0.0033|
|Cyprus|0.0904|0.0015|0.0531 ***|0.0878 **|0.0367|-0.0188|0.2113 ***|-0.9920 ***|-0.9209 ***|-0.0885|
|Finland|0.8250 ***|0.0021|0.0119|0.0473|0.0849 ***|-0.0856 ***|0.1558 ***|0.4033|-0.5851|-0.0177|
|France|0.6918 ***|-0.0209|-0.0106|0.0439|0.0530 **|-0.0284|0.2247 ***|0.0292|-0.2549|-0.0849 **|
|Greece|0.5214 ***|0.0037|0.0838 ***|-0.0314|0.0724 **|-0.1198 ***|0.2156 ***|-0.0665|0.5234 ***|-0.0628|
|Ireland|0.8503 ***|-0.0586 *|0.0166|0.0373|0.0042|-0.2083 ***|0.5183 ***|0.6455|-0.8552 ***|-0.0607|
|Italy|0.5061 ***|0.0202 *|0.0437 ***|-0.0024|0.0647 ***|-0.0986 ***|0.1283 ***|-0.3768|-0.6931 ***|-0.1004 ***|
|Luxembourg|0.8991 ***|0.0747 *|0.0360 *|0.1702 *|-0.0127|-0.2062 ***|0.2774 ***|0.9738 ***|0.5804|-0.1755 ***|
|Portugal|0.4245 ***|0.0107|0.0538 ***|-0.0248|0.0449|-0.1718 ***|0.2752 ***|-0.0309|-0.3417|-0.1275 *|
|Spain|0.6515 ***|0.0193|0.0808 ***|-0.0601 ***|0.0481 *|-0.1776 ***|0.0800 ***|0.2004|-0.4026|-0.1255 ***|
|Sweden|0.0714 ***|-0.0047 **|-0.0042 *|-0.0086 *|0.0066|0.0020|0.0391 ***|0.5376|0.9512 ***|-0.0063|
|United Kingdom|0.8818 ***|-0.0262 *|0.0272 **|-0.0373 **|0.0724 ***|-0.1529 ***|0.1969 ***|0.8422 ***||0.0365|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

5
| POLICY RESEARCH WORKING PAPER 3097

# Abstract

The World Bank's new environment strategy advocates cost-effective reduction of air and water pollutants that are most harmful to human health. In addition, it addresses threats t- .'e livelihood of over one billion people who live on fragile lands-lands that are steeply sloped, arid, or covered, i-" nntur:, forests. The new approach will require accurate information about environmental threats to health and livelihood, as well as an appropriate resource-allocation strategy.

Drawing on recent research at the World Bank and elsewhere, this paper attempts to apply an optimal investment approach. It develops a rule for optimal cross-country resource allocation that reflects the Bank's investment policy. Using this rule, the paper estimates optimal country shares of the Bank's environmental investments from two sets of variables: threats from outdoor air pollution, water pollution, and fragile lands; and estimates of the likelihood that Bank projects will succeed. The paper combines the country shares with the Bank's investment data to estimate optimal country allocations for each environmental problem. Finally, it aggregates the country results to allocations for the major regions in which the Bank operates.

Combining optimal investments for pollution and fragile lands, it finds that the largest share of total investment goes to East Asia (44 percent), followed by South Asia (21 percent) and Sub-Saharan Africa (19 percent). Other regions get significantly lower shares.

This paper-a joint product of Infrastructure and Environment, Development Research Group, and the Environment Department-is part of a larger effort to implement the World Bank's new environment strategy. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Yasmin D'Souza, room MC2-205, telephone 202-473-1449, fax 202-522-3230, email address ydsouza@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at pbuys@worldbank.org, sdasgupta@worldbank.org, cmeisner@worldbank.org, kpandey@worldbank.org, dwheeler@worldbank.org, kbolt@worldbank.org, khamilton@worldbank.org, or lwangl@worldbank.org. July 2003. (39 pages) _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 view of the World Bank, its Executive Directors, or the countries they represent._ Produced by Partnerships, Capacity Building, and Outreach
Table 4: Probability That Households Have Savings

||(1)<br>FD|(2)<br>POS|(3)<br>Business|(4)<br>Gold|(5)<br>Real Estate|
|---|---|---|---|---|---|
|Kerala*Post|-0.160<sup>_∗∗∗_</sup>|0.208<sup>_∗∗∗_</sup>|-0.003|-0.019<sup>_∗∗∗_</sup>|0.009|
||(0.017)|(0.017)|(0.003)|(0.005)|(0.006)|
|HH FE|Yes|Yes|Yes|Yes|Yes|
|Month*Year FE|Yes|Yes|Yes|Yes|Yes|
|Kerala*Timetrend|Yes|Yes|Yes|Yes|Yes|
|N|26264|26255|26265|26267|26267|
|Households|7967|7964|7967|7968|7968|

Note: Standard errors are clustered at the Household level. Kerala*Post denotes the interaction dummy for households residing in Kerala during the months post May 2018. FE = fixed effects; HH = households; FD = commercial bank deposits; POS = Post Office accounts. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001 Table 5: Probability That Households Had Outstanding Borrowing for Six Purposes

||(1)<br>Housing|(2)<br>Education|(3)<br>Medical|(4)<br>Consumption|(5)<br>CD|(6)<br>Business|
|---|---|---|---|---|---|---|
|Kerala*Post|0.030<sup>_∗∗∗_</sup>|0.003|0.016<sup>_∗_</sup>|-0.081<sup>_∗∗∗_</sup>|-0.009|0.001|
||(0.008)|(0.006)|(0.007)|(0.020)|(0.012)|(0.006)|
|HH FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Month*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Kerala*Timetrend|Yes|Yes|Yes|Yes|Yes|Yes|
|N|26,267|26,267|26,267|26,267|26,267|26,267|
|Households|7,968|7,968|7,968|7,968|7,968|7,968|

Note: Standard errors are clustered at the household level. Kerala*Post denotes the interaction dummy for household residing in Kerala for the months post May 2018. HH = households; FE = fixed effects; CD = commercial bank loans. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001 in table 5 show that they were more likely to have outstanding borrowing for housing and medical purposes. In particular, the coefficient of housing in column (1) is highly statistically significant. Outstanding borrowing for housing includes both construction of new residential property and renovation. The increase in this probability likely reflects the need to rebuild or repair damage to residential properties due to the disaster as well as inadequate or delayed receipt of relief funding.

period. The coefficient of the interaction term from estimates of equation (5) for households acquiring these assets in the past 120 days is highly significant and negative. Results are available upon request.

30
income)<sup>21</sup> over both our full sample of 699 national poverty lines and on our sample of 104 poverty lines from circa 2011. Throughout this paper, the regression analysis weights countries equally. For the circa-2011 sample, where there is only one line per country, this means the weight for each observation is one. For the full sample, where countries have varying number of observed poverty lines in our sample, the observation weight is the inverse of the number of poverty lines for each country. This prevents our analysis from assigning greater weight to countries simply because they have more household surveys available.

Our preferred sample is the full sample primarily because this offers greater estimation power. The full sample allows us to observe both variation in poverty lines across countries of differing wealth and changes in poverty lines over time as a country grows richer. Leveraging both sources of variation allows us to improve the precision of our estimates, which is particularly useful to assess whether the relationship varies significantly for low-income countries. The analysis based on the smaller sample of lines from circa 2011 provides evidence of the stability of the relationship over time and also helps address concerns of relevance with more recent data.

_Setting the SPL Parameters_ Panel A of Table 2 presents the findings from regressing the value of the poverty lines (in levels) on national wellbeing. Columns 1 and 2 (and columns 4 and 5) contrast the fit of the

> 21 For most countries in our sample of national poverty lines, consumption is the underlying measure of wellbeing. For about one-third, wellbeing is assessed by income and in these cases, we use median and mean income values. See Ferreira et al (2016) for more discussion on the underlying measures of wellbeing used for global poverty estimation. In all cases, the analysis in this paper matches the national poverty line with the measure of wellbeing that is used to assess poverty status.

14
mon trend. Next, we assess the plausibility of this assumption.

# **Exogeneity of the Timing of SHG Creation**

In 1994, PRADAN opened an office in the town Keonjhar and started operating in the poorest block, namely Banspal, which is southwestwards of the town (Figure 1). The initial focus was on agriculture, as villagers owned some land and small, but perennial rivulets were available to provide irrigation without the need of major investments. A few years later, in 1998, PRADAN started promoting SHGs to provide extra resources to strengthen livelihoods.

In the following years, PRADAN identified the poorest regions in the contiguous areas and expanded its activities. At first, two new offices were opened: one in Suakati, in the Banspal block in 1998 (Figure 1), and a second in Turumunga, in the Patna block in 1999 (Figure 2). Later, from 2000 onwards, employees based at the Keonjhar town office started activities in the Keonjhar Sadar block, located to the west of Keonjhar (Figure 1), and in the adjacent Karanjia block, which is located in the Mayurbhanj district (Figure 3).

PRADAN’s decision about where to operate was based on the suitability of an area for its agricultural programs. We surveyed all the wards where PRADAN operates in the Keonjhar and Mayurbhanj districts of Odisha. Therefore, we believe that, in terms of our research question, the program placement bias is limited. However, the program timing bias is a concern if the wards in which PRADAN started working earlier within blocks are systematically different with respect to the outcomes of interest.

Figures 1, 2 and 3 show the villages in which PRADAN promoted SHGs. We show villages instead of wards, as ward-level maps do not exist. The villages are a good approximation, as 75% of the villages encompass exactly the same territory as wards and only 2.8% of the wards cover several villages. We colored the villages according to the mandate during which a first SHG was created in the village: 1997-2001 versus 2002-2006.<sup>22</sup> We distinguish between these two periods, as they correspond to the election periods during which SHGs were created (see Table 4). The

> 22In case there are several wards in the village, we use the mandate during which the first SHG was created in any of the wards that belong to that village.

32
before 1966, this information is obtained by using retrospective smoking histories constructed from the smoking supplements of National Health Interview Surveys conducted between 1978 and 2000. The conclusion from this analysis is that the smoking prevalence among more educated individuals, college graduates in particular, declined earlier and most dramatically than any other education category. The decline for college graduates started in 1954, four years after the medical consensus on the health consequences of smoking and 10 years before the publication of the first Surgeon General’s Report on this issue. This suggests that they had easier access to the information and/or were more able to process that information.

The decision to quit smoking—usually taken after education is completed— provides a clearer margin for analysis than smoking initiation, which generally occurs before schooling levels are definitive. The paper further develops a detailed study of the incidence of smoking cessation using a proportional hazard model, which shows the strong effect of education on the probability for smokers to quit smoking at any point in time. The analysis for all birth cohorts of smoking age during the 1950–70 period, when the information on the risks associated with tobacco was not uniform, indicates that it was during this period that the effect of education was the strongest, independent of age. The instrumental variable approach used―college attendance as a draft avoidance strategy during the Vietnam War—suggests that a college education affects decisions about whether to smoke or stop smoking, even after controlling for income.

The paper is structured as follows. Section 2 presents the results from the analysis of the smoking prevalence in the United States from 1940 to 2000. Section 3 focuses on an analysis of the incidence of smoking cessation among smokers. Section 4 contains the results of the instrumental variable strategy that exploits, as an instrument for education, college attendance as a draft avoidance strategy during the Vietnam War. Section 5 concludes.

# **2. Evidence from Smoking Histories in the United States, 1940–2000**

It is well known by social scientists that more educated people are less likely to smoke (Wald 1988). The literature about the negative correlation between education and tobacco use has become a key element in the debate about whether education is causal in improving health.<sup>2</sup> The study of smoking habits and education prior to the arrival of the information about the dangers of smoking for the health have been less studied due to lack of data (Ferrence 1989; Harris 1983; United States Government 1980). Before 1950, even though

> 2 See Berger and Leigh (1989); Chaloupka and Warner (2000); Currie and Moretti (2003); Farrell and Fuchs (1986); Fuchs (1982); Grossman (1975); Kenkel (1991); Meara (2001); Sander (1995); Viscusi (1990, 1992).

2
15

# **4.2-Sensitivity to Child Age Definition**

This section examines the effects of child gender on adult consumption with a lower definition of child age than that of 14 employed in the HICES 2005-06. I exclude from the sample of section 4.1 any household containing a child above 12 years of age. We now have four child age groups in (1) and (2), and the missing age group is =<10, leaving only three child age variables in (1) and (2) for 0-2, 3-6, 7-9 years. The child gender effects for this new sample is summarized in table 4, column 2, headed “child <13”; and the detailed results appear in table 7.

Table 4/column 2 indicates that the gender estimates are all positively significant at 1% level. Turning to table 7 for the details, consumption elasticities look reasonable, household size effects are positively significant throughout. Child age composition exerts no significant effect on tobacco, or adult clothing, but there is a significant negative age composition effects by all age groups on coffee, and that of 3-6 years on personal services. Urban/rural location is negative throughout, though insignificant for adult clothing. The safety net dummy tends to affect significantly consumption except for adult clothing. The regional effects, as before, vary in direction, but SNNP tends to have the largest impact.

Overall, the results suggest robustness to the age definition of children employed, and offer some support for the suitability of selected commodities as goods exclusively consumed by adults.

# **4.3-FHH**

The share of FHH in the HICES 2005-06 is 31.4% of the survey households. However, some 20% of the FHH are of nuclear type, although those with biological children constitute a sub-group of just about 10% within it, resulting in a rather small sample size of 442 households<sup>14</sup> . Thus, the influence of FHH on child gender estimates

> <sup>14</sup> The definition of a FHH adopted above results in 37.1% of the all households surveyed in Tigray, well within the range reported in Desta al et. (2006) of 31-40% for this region. Yet when restricted to nuclear households, they account for only 2% of the total in this region, less than a dozen in numbers. More generally, nuclear observations constitute 52% of the survey observations (11166 nuclear households with children), of which only 4% (442) are FHHs.
<!-- Start of picture text --> 280<br>220<br>ig<br>E eo<br>190<br>100<br>FLEES EPP EPP E FIL EPAS EPAS<br>Month-year<br>—NPi ——Food CPI<br><!-- End of picture text --> <!-- Start of picture text --> 140<br>190<br>$12<br>£110<br>100<br>90<br>” 2 é 2 é ? ¥ é ¥ é é » ¥ é ¥ é é - ¥ é ¥ é é » ~ é ~ é<br>Month-year<br>——NPI ——FoodCPICPI<br><!-- End of picture text --> <!-- Start of picture text --> 140 280<br>190 220<br>$12 ig<br>£110 E eo<br>100 190<br>90 100<br>” 2 é 2 é ? ¥ é ¥ é é » ¥ é ¥ é é - ¥ é ¥ é é » ~ é ~ é FLEES EPP EPP E FIL EPAS EPAS<br>Month-year Month-year<br>——NPI ——FoodCPICPI —NPi ——Food CPI<br>:<br>150 bad<br>140 140<br>E12ie £i120 lt<br>110 110<br>a - < < ro FFxx oS ros KKPrPr oS ro FSSeSeS ss “ s ©s Ss 2 2 2 Hw © © SB A A<br>SF SS LCE FFE FEP EOF EPE EFSF of<br>Month-year Month-year<br>—NPI —FeodCPICPI —wNP| —FoodCPI<br>6 20<br>m4 g 16<br>i, En<br>pe Es<br>62 S 4<br>“4 2 SS PG © PY © ww A A ° SS SPP GM © SY & Sw A XA<br>ee ee<br>Month-year Month-year<br>6 6<br>g4<br>0 P<br>6-2 6<br>40<br>x<br>oF gh gh oF Pe oF ye??? vga? oP wh ® re eeS y eYX LY FSSTG 2 DO FXSY KS© © XK© A<br>Month-year Moet yerr<br><!-- End of picture text -->

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_Figure 3: Equity and fixed income markets: 1998 to 2006_ <!-- Start of picture text --> Index 1/1 1996 = 100 Pct<br>400 16<br>Copenhagen<br>Stock Exchange<br>(lhs)<br>350 14<br>300 12<br>250 10<br>DAX (lhs)<br>200 8 Dow Jones<br>Industrials (lhs)<br>150 6<br>Danish<br>Corporate Bond<br>Yields (rhs)<br>100 4 Danish Central<br>Government 10<br>Year Bond<br>50 2<br>0 0<br>Source: Bloomberg<br>Jan-88 Jan-89 Jan-90 Jan-91 Jan-92 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05 Jan-06<br><!-- End of picture text --> These events coincided with the introduction of new accounting rules, requiring more frequent and mark-to-market valuation of the balance sheet, and implementation of the traffic light system, which also helped underscore the difficult situation of the pension sector.

_Table 9: Return on Investments for Life Insurance Companies and General Pension Funds_

||2000|2001|2002|2003|2004|2005|
|---|---|---|---|---|---|---|
|Return on investments before tax|5.65|-1.63|1.77|6.86|10.40|14.25|
|Return on investments after tax|4.95|-1.20|1.71|6.05|9.11|12.46|
|Source: Danish FSA|||||||

The Danish experience highlights the potential advantages of introducing such regulatory and accounting changes during times where financial markets are calm. The combination of turbulent financial markets and dramatic rule changes may force individual institutions to implement myopic solutions to survive to the detriment of longterm performance maximization.

# **5 THE GROWING USE OF DERIVATIVES**

The impact of the perfect storm illustrated an essential point about the balance sheet of the Danish pension institutions, namely the mismatch between the two sides. Getting a better match on the balance sheet involves managing both liabilities and assets, but since little can be done in the short run to directly change the structure of liabilities 13