rafmacalaba/datause-encoder-data huggingface dataset:
Drawing data from a complimentary survey on government delivery of public goods and services,<sup>25</sup> we find that 95 percent of adults who never applied for an account had an _Aadhaar_ number.

Despite high coverage of Aadhaar and the launch of eKYC, which allows customers to verify their identity electronically, the share of unbanked adults remains high. This suggests the importance of ensuring documentation requirements under the JDY policy are clearly and effectively communicated to the public in order to clear up misconceptions.

# **_5.2.3 Other monetary costs_**

The survey explores two potential types of fraud: minimum deposit requirements at the time of opening an account and informal payments in the form of presents or money requested in addition to the official cost of opening an account.

Officially, there is no fee for opening a JDY account. Anyone who produces the necessary documents and shows up at the bank is eligible for a free account. Yet one in five adults who opened an account said they were required to pay extra money or presents, such as to a middleman who demanded extra payments in exchange for processing account applications, with no differences between JDY account holders and traditional account holders (Figure 7).

Among adults who opened a traditional account, 72 percent said they were required to pay a minimum deposit (Figure 7). The share was 53 percent among those who opened a JDY account– which is surprising given that there is no minimum deposit requirement under the JDY policy. In fact, the zero-balance provision is touted as one of policy's most attractive features. Most adults are not well informed about the policy and continue to attach high costs to the process of applying for an account. Marketing the policy clearly and correctly might prevent people from being misinformed.

Our aim in this section has been to provide a detailed analysis of individual account application

> 25 Demirguc-Kunt, D., L. Klapper, and N. Prasad (2016).

22
Key information from the field experiment is summarized in Table 6. The first point to note is the low cost of the ICS, which is roughly 0.01 (1 percent) of the 2012 per-capita income in Senegal (based on 2012 UN statistics for Senegal). Information from USAID (n.d.) indicates that the type of ICS used in the experiment could be expected to have a useful life of at least 2-3 years, and often 5-6 years. This implies a cost per year on the order of only 0.2 percent of annual per-capita income).<sup>12</sup> The next finding is that while firewood consumption drops considerably with use of the ICS (on the order of 31% below the baseline), fuelwood collection time is very little affected by adoption of the ICS. Bensch and Peters (2015) speculate that this could reflect, among other things, household decisions to carry less wood per collection trip, rather than reducing the total time spent on collection. This point illustrates the importance of trying to obtain field-validated data for evaluation of ICS, as opposed to just relying on the assumption that collection time dropped in proportion to wood use.

**Table 6.  Findings from a field experiment for introducing ICS in Senegal**

|Parameter|Traditional wood stove|Wood ICS|
|---|---|---|
|Stove purchase cost|0|$8-11|
|Fuelwood collection time<br>(hours per week)|23|22|
|Firewood consumption<br>(kg/week)|88|61|
|Cooking duration (hours per<br>week)|39|30|
|Percentage of households<br>with occurrence of respiratory<br>disease symptoms<sup>*</sup>|17.3|9.1|

Sources: Bensch and Peters (2015); Tijdens et al (2012); USAID (n.d.)

*Based on self-reported information of symptom occurrence over the past three months for at least one woman in the household involved in cooking.  Total number of observations = 227.

The drop in wood use is somewhat below the estimate obtained from controlled cooking tests (around 40%). This may in part reflect a typical difference between controlled and in-the-field behaviors. It could also reflect in part to a “rebound effect:” – with a more fuel-efficient stove in effect lowering the opportunity cost of cooking, households may elect to do more cooking (which could also explain the limited impact of the ICS on collection times). Support for this supposition

> 12 This rough calculation does not account for the difference between rural and national average incomes, reflecting higher incomes in urban areas.  On the other hand, the cost per household will be smaller than indicated in the text <u>given multiple income earners in the household.</u>

**14 |** P a g e
|**Indicators**|**De Jure**|**Perceived**|
|---|---|---|
|**Overall Regulation**|**60%**|**37%**|
|**Regulatory Governance**|72%|61%|
|**_Accountability_**|79%|85%|
|_Regulatory Oversight_|_67%_|67%|
|_Legal Appeals_|_100%_|100%|
|_Transparency_|_70%_|89%|
|**_Autonomy_**|92%|72%|
|_Decision-Making Autonomy_|_92%_|62%|
|_Budgetary Autonomy_|_100%_|100%|
|_Leadership Autonomy_|_75%_|75%|
|_Managerial Autonomy_|_100%_|50%|
|**Regulatory Substance**|83%|61%|
|**_Tariff Regulation_**|83%|83%|
|_Regulatory Framework for Tariffs_|_100%_|100%|
|_Determination of Tariffs_|_67%_|67%|
|**_Quality Regulation_**|100%|71%|
|_Quality of Service Standards_|_100%_|75%|
|_Quality of Service Enforcement_|_100%_|67%|
|**_Market Entry Regulation_**|67%|29%|
|_Permitting New Entrants_|_100%_|25%|
|_PPA Approvals_|_33%_|33%|

Source: Data collected for the Rethinking Power Sector Reform Project.

Decision-making autonomy is much lower de facto than de jure and this is attributable to the fact that the government has recently been influencing the tariffs determined by NEPRA and has proposed to remove all tariff setting authority to the Ministry. Managerial autonomy is also lower de facto because of the transfer of administrative control of NEPRA to the Ministry of Water and Power.

Notably, Pakistan’s tariff regulation system is perceived to function in practice close to what is intended on paper. However, both the quality of service standards and the quality of service enforcement have lower de facto scores than de jure scores. Utilities try to comply with these standards but struggle to do so due to technical challenges, while fines to meet these standards are seldom imposed, and financial incentives to meet customer service standards are ignored.

Market entry regulation has a much lower score de facto than de jure in respect of permitting new entrants. Solar and wind have faced difficulties in getting through the initial stages of approval. More letters of intent associated with land allocation rights than the land available or the interconnection potential of the grid have been issued, so that only developers with strong links to the government have had land identified and allocated so that they can move on to 56
Table 6: Cross-Country Comparison: Top of the Income and Wealth Distributions (2010)

||Inc|ome|Micro|Data<br>We|alth|Macro Data<br>Income (p.c.)|
|---|---|---|---|---|---|---|
|Countries|Top 1%|Top|10%|Top 1%|Top 10%|WDI|
|SSA:|||||||
|Malawi|20%||50%|24%|57%|359|
|Tanzania|15%||51%|24%|72%|524|
|Uganda|31%||65%|28%|69%|471|
|Rich:|||||||
|US|20%||48%|34%|71%|48,377|
|Britain|15%||42%|28%|70%|38,363|
|France|9%||33%|24%|62%|40,706|
|Sweden|7%||28%|20%|59%|52,076|
|Emerging:|||||||
|India|12%||_n.a._|_n.a._|_n.a._|1,417|
|Indonesia|13%||_n.a._|_n.a._|_n.a._|2,946|
|China|11%||_n.a._|_n.a._|_n.a._|4,433|
|South Africa|17%||_n.a._|_n.a._|_n.a._|7,175|
|Argentina|17%||_n.a._|_n.a._|_n.a._|11,460|
|Colombia|20%||_n.a._|_n.a._|_n.a._|6,179|

_Notes:_ The figures for rich and emerging countries are retrieved from Piketty (2014). All numbers refer to 2010, except for Argentina, which refers to 2005. The construction of the measures of household income and wealth for SSA countries is discussed in Section 2, with further details in the online appendix.
# **Competition and Demographics**

Mohammad Amin* World Bank Keywords: Competition, Demographics JEL: D24, L11, L81 ________________________

> *Enterprise Analysis Unit, the World Bank, Washington DC, 20433. Email: <u>mamin@worldbank.org. Phone: (202)-473-1915. I would like to thank Simeon Djankov, Alvaro</u> Gonzalez, Ernesto Lopez-Cordova and Siddharth Sharma for helpful comments. All errors are my own.
Table A5: Impact of the decree on decision phase (including adjustment period)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Duration<br>of decision<br>stage (in<br>days)|Hazard<br>ratio -<br>fnishing<br>decision<br>stage|Likelihood<br>of decision<br>comple-<br>tion in 1<br>month<br>|Number<br>of decision<br>stage<br>hearings|Decision<br>stage<br>likelihood<br>of being<br>heard|
|Entered after decree|3.779|1.072|-0.041|-0.127|0.020|
|application (_β_)|(6.581)|(0.076)|(0.040)|(0.294)|(0.025)|
|Trend|0.746***|0.986***|-0.007***|0.022***|-0.005***|
||(0.172)|(0.002)|(0.001)|(0.007)|(0.001)|
|Interaction|-2.330***|1.005|0.006**|-0.050**|0.004*|
||(0.399)|(0.005)|(0.002)|(0.019)|(0.002)|
|P-value 6pWCBoot|0.696||0.641|0.690|0.548|
|Chamber FEs|Yes|Yes|Yes|Yes|Yes|
|Calendar month FEs|Yes|Yes|Yes|Yes|Yes|
|Without adj. period|No|No|No|No|No|
|Pre-mean|63.442||0.496|2.625|0.772|
|Pre-sd|82.632||0.500|3.445|0.248|
|R-Squared|0.064||0.155|0.027|0.328|
|Observations|3963|4214|4214|4214|4214|

<u>Notes:</u> Estimates of case-level impacts of the reform on decision-stage proceedings. See Notes for cols 2-4, Table A-1. 4214 observations for decision stage outcomes, except col 1 (censoring).

59
Table 7: Linear models of alternate measures of customs <u>performance</u><sup>_a_</sup>

||log Import Cost (TAB)|log Export Cost (TAB)|Customs (LPI)|
|---|---|---|---|
|lngdppc|-1.044<br>|-1.124|-0.438|
||(0.625)* <sup>_b_</sup>|(0.522)**|(0.230)*|
|lngdppc2|0.065|0.068|0.024|
||(0.038)*|(0.032)**|(0.014)*|
|lngdp|0.040|0.042|0.065|
||(0.079)|(0.067)|(0.029)**|
|lnsqkm|0.026<br>|0.002<br>|0.001<br>|
||(0.049)|(0.044)|(0.019)|
|landlocked|0.037|-0.080|-0.043|
||(0.168)|(0.148)|(0.065)|
|island|0.049|-0.071|0.006|
||(0.179)|(0.154)|(0.070)|
|wto|0.186|0.169|0.114|
||(0.232)|(0.205)|(0.097)|
|oecd|-0.235|-0.295|0.007|
||(0.331)|(0.293)|(0.122)|
|WGI - corrupt|-0.089|-0.053|0.041|
||(0.314)|(0.257)|(0.107)|
|WGI - efect|-0.226|-0.243|0.248|
||(0.340)|(0.285)|(0.130)*|
|WGI - stable|-0.307|-0.281|0.003|
||(0.119)**|(0.102)***|(0.047)|
|WGI - regqual|-0.256|-0.157|0.010|
||(0.202)|(0.173)|(0.079)|
|WGI - voice|0.224|0.091|-0.056|
||(0.144)|(0.126)|(0.055)|
|Information|-0.090|-0.180|-0.064|
||(0.220)|(0.189)|(0.086)|
|Involvement|-0.016|0.008|0.041|
||(0.146)|(0.120)|(0.050)|
|Adv. Rulings|-0.329|-0.114|0.031|
||(0.141)**|(0.117)|(0.049)|
|Appeals|0.222|0.257|-0.035|
||(0.237)|(0.179)|(0.073)|
|Fees & charges|-0.055|0.045|0.025|
||(0.169)|(0.139)|(0.058)|
|Documents|-0.074|-0.245|-0.048|
||(0.224)|(0.194)|(0.080)|
|Automation|-0.315|-0.204|0.054|
||(0.174)*|(0.143)|(0.062)|
|Procedures|0.166|0.199|0.025|
||(0.327)|(0.253)|(0.106)|
|Coop. - internal|-0.141|0.008|-0.026|
||(0.128)|(0.109)|(0.047)|
|Coop. - external|-0.176|-0.079|-0.017|
||(0.151)|(0.107)|(0.044)|
|Consularization|-0.045|-0.025|0.024|
||(0.100)|(0.089)|(0.038)|
|Governance|0.319|-0.102|0.032|
||(0.203)|(0.179)|(0.083)|
|Pseudo R-squared|0.457|0.473|0.846|

Notes:<sup>_a_</sup> Combined results from estimates over 80 imputations. _b_ standard error is reported in brackets below estimated parameter, *** indicates 1% significant, ** 5% significant and * 10% significant. Trade infrastructure variables included in the regressions, but coefficients not reported for space reasons.

30
Our dependent variables of interest are: (1) an ordinal measure capturing the intensity and type of punitive censorship incidents, and (2) an indicator of the total number of incidents of media censorship (count data of salient events) (see Figures 1–5 and Appendix B for descriptive statistics), covering 196 countries between 2001 and 2015. We selected these variables because they proxy two separate but complementary indicators of the prevalence and severity of censorship across countries and over time.

<!-- Start of picture text --> Distribution of censorship events by severity<br>Lawsuit Fines EvictionAccreditation denialBlock License removal Threats of physical violenceBanIntimidation Assault Jail Death Other<br>1,500<br>1,000<br>500<br>Total number of censorship events<br>0<br><!-- End of picture text --> Figure 4: Distribution of censorship events by their severity (2001–2015) <!-- Start of picture text --> Trends in the severity of censorship worldwide (2001-2015)<br>2000 2005 2010 2015<br>Year<br>7.8<br>7.6<br>7.4<br>Severity of censorship worldwide (mean)7.2<br><!-- End of picture text --> Figure 5: Time trends of censorship severity worldwide (2001–2015) 28
# **1. Introduction**

The number of forcibly displaced persons worldwide has more than doubled over the past three decades, reaching 84 million (or more than 1% of the world population) by the middle of 2021. About 15% of this figure is hosted by European countries, with Turkey and Germany being among the largest refugee-hosting countries over the world.<sup>1</sup> One thorny issue faced by host countries, richer and poorer alike, is the low employment rate of refugees compared with those of citizens and other immigrants (Dustmann et al., 2017), a phenomenon that can persist even in the long run (Brell et al., 2020). Lack of refugee integration not only generates substantial financial costs for the host country but can lead to other social issues such as increased crime levels and reduced schooling outcomes of children in the host population (Piopiunik and Ruhose, 2017; Di Maio and Nisticò, 2019). As such, policies that improve the labor market outcomes of refugees can lead to increased returns for both refugees and their hosts.

Poor mental health has been shown to have detrimental impact on labor market outcomes (e.g., Chatterji et al., 2011). Since mental health is an important component of an individual’s stock of human capital, mental illness may impair individuals’ ability to obtain employment and maintain their earnings, by affecting factors such as productivity, motivation, and social relationships (Heckman et al., 2006; Cunha and Heckman, 2007). Having experienced conflicts and persecution first-hand, refugees are especially vulnerable to mental health issues. Indeed, more than one-fifth of refugees are estimated to suffer from anxiety, depression, or posttraumatic stress disorder (PTSD), and forced migrants could be 10 times more likely to have mental health issues compared to the local population (Fazel et al., 2012; Bogic et al., 2015; Moya, 2018).

We provide the first study to examine the relationship between mental health and refugees’ labor market outcomes. We use Australia as a uniquely interesting case study. The country has

> 1 <u>https://www.unhcr.org/figures-at-a-glance.html and UNHCR (2021).</u>

2
_ForContij_ = α 1 _Insthosti_ + α 2 _CompAdvij_ + α 3 _Collinksij_ + α 4 _Borderij_ + α 5 _Comlang ij_ + α 6 _Distanceij_ + α 7 _Tradeij_ + α 8 _LegalDif ij_ + α 9 _GDPhosti_ + α 10 _FinDepthhosti_ (1) + α 11 _Entryres_ + α 12 _GDPsource j_ + α 13 _GDPcapsource j_ + α 14 _DRegion_ + ε _ij_ where _i_ refers to the host country and _j_ to the source country. We estimate our gravity model using Tobit to account for the many zeros in the dependent variable. In addition, the standard errors are corrected for heteroskedasticity,

# **4. Results**

# _Does competitive advantage matter?_

Using regression model (1) we test whether familiarity with the type of business climate relative to its competitors, matters in a bank’s decision which country to enter. Before turning to the regression results, we first estimate the model excluding the competitive advantage variable but including the measure of institutional quality in the host country and the absolute difference between institutional quality in the host and source country respectively. The latter variable is constructed by taking the simple average of the absolute difference of each of the six governance indicators.

Table 1 presents the results. To aid the economic interpretation we show, instead of parameter estimates, the marginal effects of the unconditional expected value of the dependent variable, E(y), where y=max( _a_ , min(y*, _b_ )) where _a_ is the lower limit for left censoring (0) and _b_ is the upper limit for right censoring (100). The marginal effects are calculated at the mean of the independent variable, except when the independent variable is a dummy in which case the marginal effect is calculated as the change in the dummy variable from 0 to 1. The marginal effects capture the combined effect of the impact of the explanatory variable on the probability of entering the host country as well as on the amount of FDI. The mean of the dependent variable is equal to 0.57 percent.

The first two columns in the table show that both institutional quality of the host country as well as the difference in institutional quality have no statistically significant impact on cross-border banking activity. In fact, the variable capturing differences in institutional quality has a, counterintuitive, positive sign, although not statistically 11
the 10.22 critical value for 10% maximal relative IV bias, and the 10.41 critical value for maximal IV size bias in Wald tests. Overall the evidence is fairly strong in rejecting the null of weak instruments. Nevertheless, we not only report results from 2SLS in Table 5, but also for an alternative IV estimator, that is more robust to the presence of weak instruments, namely LIML (limited information maximum likelihood). In contrast to 2SLS estimates, LIML estimates are consistent under a weaker set of assumptions on instrument strength (Chao and Swanson, 2005).

Second-stage results from 2SLS are presented in equation 5-3. The morality-ability coefficient is positive and significant at the .01 level as in OLS, and almost twice as large (6.59 compared to 3.62) than in the similar OLS specification of equation 2-4. The morality proxy is positive and significant at the .05 level. Its coefficient is almost three times as large as its OLS counterpart (.047, compared to .017 in equation 2-4), perhaps reflecting a correction for attenuation bias associated with measurement error in our morality indicator.

Both morality and the morality-ability correlation also exhibit positive and significant coefficients in LIML estimation in equation 5-4, with magnitudes slightly larger than in 2SLS. In both 2SLS and LIML, the null hypothesis that the instruments are valid is supported; p-values for the relevant test of overidentifying restrictions are .77 and .79 respectively. As with the OLS results, the 2SLS and LIML results change very little if we weight countries by their number of survey respondents or if we drop Tanzania.

# _D. Alternative Morality Proxies in the WVS_

In Table 6 we report results for other possible morality proxies included in the WVS. Several other questions in the WVS pertain to work-related “values,” but they focus on the decision to 28
the housing market in 2004 and 2005.<sup>33</sup> Specifically, the Fed kept pursuing loose monetary policy, even while it gave assurances to the markets on its willingness to maintain easy monetary conditions and to step in to provide liquidity in case the financial markets had problems, the socalled ―Greenspan put.‖<sup>34</sup> These assurances, along with a range of housing policies discussed in further detail below, helped to boost lending significantly, including to creditors who were barely creditworthy.<sup>35</sup> As noted by Rajan (2010), ―Sustained easy monetary policy that is maintained while jobs are still scarce has the effect of increasing risk taking and inflating asset-price bubbles, which again weaken the fabric of the economy over the longer term.‖ Household borrowing went up from 66 percent of GDP in 1997 to 100 percent of GDP a decade later. The protracted period of low short-term policy interest rates in combination with financial innovation also lowered long-term interest rates as a result of low inflationary expectations and a perceived decline in the riskiness of financial assets, and led to a situation of excess liquidity which supported an incipient housing boom since 2002 (Figure 36).

<!-- Start of picture text --> Figure 36: Changes in US Housing Prices by the S&P/Case-Schiller Indices<br>24%<br>20%<br>16%<br>12%<br>8%<br>4%<br>0%<br>1953 1958 1963 1968 1973 1978 1983 1988 1993 1998 2003 2008<br>-4%<br>-8%<br>-12%<br>-16%<br>-20%<br>-24%<br>Source: Schiller (2005); authors' calculations.<br>Percentage change, year ago<br><!-- End of picture text --> 33 Jarocinski and Smets (2008): 319-65.

34 A term coined by the Financial Times in an article of January 4, 2001, which referred to Greenspan’s statement that the Fed would intervene ex post to smoothen the effects of the burst of a bubble, but could not know ex ante about it.

35 Obstfeld and Rogoff (2010) also note that the overly accommodative stance of the Fed’s monetary policy starting in 2001 played a key role in the expansion of the housing market and of global imbalances.

43
# **Saving for Old Age**

## **Asli Demirgüç-Kunt**

**Leora Klapper Georgios A. Panos**

JEL Classification: C80, D14, D31, E21, J32.

Keywords: Saving, old age, financial inclusion.

> * Demirgüç-Kunt, World Bank, Ademirguckunt@worldbank.org; Klapper, World Bank, lklapper@worldbank.org; Panos, Adam Smith Business School, University of Glasgow, Georgios.Panos@glasgow.ac.uk.  We thank Saniya Ansar, Ehui Adovor and Christiana Sintou for excellent research assistance and participants at the 2015 Symposium by the Pension Research Council on Implications of the New Regulatory Order for Retirement System Risk Management for valuable comments.
# **6.  Summary and Conclusions**

In this paper, we have constructed a spatial panel database of cyclone tracks and coastal landfalls in Bangladesh, West Bengal and Odisha. For 1960-2016, our tracking data come from the Bangladesh Meteorological Department (BMD) for cyclones with Bangladesh landfalls and the India Meteorological Department (IMD) for cyclones with landfalls in West Bengal and Odisha. For storms before 1960, our data come from country reports in the global IBTrACS database maintained by the Global Data Center for Meteorology of the US National Oceanic and Atmospheric Administration. We determine each storm’s landfall as its first intersection with an ordered line of coastline points, spaced at .001 decimal degrees (approximately 100 meters). We identify each cyclone’s primary coastal impact zone as the set of coastline points lying within the cyclone’s radial distance from its landfall point to its zone of maximum wind speed. Although we confine our exercise to Bangladesh and the two Indian coastal states, our methods are general and could be expanded to an arbitrarily-large set of coastal locations.

We use the database for an analysis of cyclone strike locations and impact zones in seven historical periods: 1877-1900, 1901-1920, 1921-1940, 1941-1960, 1961-1980, 1981-2000 and 2001-2016. Although we believe that our results have independent interest, we highlight their implications for socioeconomic analysis of household and community responses to cyclonic storm risks. Episodic, short-run responses to some individual storms are well-documented. Such anecdotal evidence certainly provides many useful insights, but georeferenced panel data are required for systematic comparative research on coastal adaptation.

In this context, robust assessment of causal factors requires significant variation in cyclone impacts, both across coastal areas and over time. The results of our seven-period analysis indicate that such variation characterizes the coastal zone. During each 20-year period, some areas have 20