rafmacalaba/datause-encoder-data huggingface dataset:
42 Table 13 (continued)

||Stationary<br>po|pulation|||Stationary|population||
|---|---|---|---|---|---|---|---|
||||Year||||Year|
|Country or economy|A<br>Millions<br>19|s X of<br>85pop|<br>when<br> NRR=1|Country or economy|Millions|As X of<br>1985 pop|when<br>NRRs1|
|Jamaica|4|189|2005|Philippines|137|246|2015|
|Martinique|0.3|87|2000|Singapore|3|114|2030|
|Mexico|187|238|2010|Thailand|99|191|2000|
|Montserrat|0.02|150|2015|Viet Nam|168|273|2015|
|Netherlands Antilles|0.3|157|2010|East and Southeast||||
|Nicaragua|13|382|2025|Asia total|2938|178|2035|
|Panam|4|205|2005|||||
|Paraguay|10|281|2015|Afghanistan|76|418|2045|
|Peru|48|250|2015|Bangladesh|342|340|2030|
|St. Kitte<br>and Nevis|0.1|128|2015|Bhutan|4|323|2035|
|St. Lucia|0.4|257|2015|India|1698|222|2010|
|St. Vincent and Grenadines|<br>0.2|209|2010|Iran, Islanic<br>Rep. of|169|382|2025|
|Suriname|1|235|2010|Maldives|1|411|2030|
|Trinidad and Tobago|2|182|2010|NepaL|63|377|2035|
|Uruguay|4|142|2000|Pakistan|423|440|2035|
|Virgin<br>Islands (U.S.)|40|231|2005|Sri Lanka|30|189|2005|
|Venezuela|0.2|204|2005|South Asia total|2805|265|2045|
|Other Latin America|0.4|216|2010|||||
|LatinAmerica and the||||Bahrain|1|237|2010|
|Caribbean total|912|229|2030|Cvprus|1|137|2005|
|||||Gaza Strip|3|517|2030|
|Canada|28|109|2030|Iraq|75|471|2025|
|United States (50 states)|279|116|2030|Israel|7|176|2005|
|Puerto Rico|5|148|2000|Jordan|13|357|2020|
|Other Northern America|0.1|130|2030|Kuiait|5|281|2015|
|Northern America total|311|116|2030|Lebanon|5|190|2010|
|||||Oman|5|427|2030|
|Asia|6118|217|2045|Qatar|1|363|2025|
|Brunei|1|246|2010|Saudia Arabia|54|471|2025|
|Burma|102|275|2020|Syrian Arab Rep.|42|401|2020|
|China (excl.<br>Taiwan)|1695|163|2000|Turkey|112|222|2010|
|Taiwan, China|31|160|1985|United Arab Emirates|4|270|2020|
|Hong Kong|7|124|2030|Yemen Arab Rep.|39|491|2040|
|Indonesia|356|218|2010|Yemen, People's Dem. Rep.|<br>9|410|2035|
|Japan|119|99|2030|<br>Southwest Asia total|<br>375|327|2040|
|<br>Kampuchea, Oem.|20|277|2035|||||
|Korea, Dem. People's Rep.|of<br>49|239|2015|Europe and U.S.S.R.|881|114|2030|
|Korea, Rep.|65|159|1985|Albwuie|6|204|2005|
|Lao People's Dem. Rep.|15|428|2035|AusWt a|6|80|2030|
|Macao|1|166|2005|Belgium|8|80|2030|
|Malaysia|33|213|2005|Bulgaria|10|107|2030|
|Mongolia|6|293|2020|Charnel Islands|0|80|2030|
P a g e <u>|</u> **47**

<!-- Start of picture text --> 1200 Ahmedabad<br>1000<br>800 Bunglows<br>Apartments<br>Twin / Row Houses<br>MIG HIG<br>600<br>Walled City<br>EWS LIG<br>Chawls<br>400 Other Slums<br>River Front Slums<br>Pavement Dwellers<br>200<br>0<br>Monthly Household Income ‐ INR<br>Households in '00 (Total 0.9 million)<br>2500 5000 7500 10000 12500 15000 17500 20000 22500 25000 27500 30000 32500 35000 37500 40000 42500 45000 47500 50000 52500 55000 57500 60000 62500 65000 67500 70000 72500 75000 77500 80000 82500 85000 87500 90000 92500 95000 97500 100000 102500 105000 107500 110000 112500 115000 117500 120000<br><!-- End of picture text -->

**Figure 13: Ahmedabad Household Income and Housing Typology**

The market equilibrium in Ahmedabad also shows a relatively small share of public housing options, slightly above 10 percent of the total stock of housing which are affordable mostly for the middle class. The walled city and the chawls<sup>17</sup> provide formal housing solutions for a much more substantial share of lower income groups, amounting to 35 percent of total. Formal housing is affordable to the top 40 percent of households. Clearly supplies are far less constrained, land prices more manageable and affordability less of a problem. Yet, even in fortunate Ahmedabad, while less than 5 percent of the population is below the All India urban poverty level, a much larger percentage is living in slums. It is also striking that in a city where household income has grown so rapidly for the last several years<sup>18</sup> , the stock of compliant formal housing is still quite small.

# **Recommendations for national policy**

# The case studies of Ahmedabad and Mumbai demonstrate that:

> 17 This housing would not be compliant with today’s standards but is still considered formal.

> 18 Estimated at 12.5 per cent in NCAER (2010)

47
environment with a different sample population. Third, the controlled experiments, as they have been undertaken up to now, do not consider any spill-over effects of access to credit by the treated individuals or enterprises to other individuals or enterprises in the economy.

# **7. Concluding remarks**

The finance and growth literature has come a long way from simple correlation and OLS regressions to dynamic panel regressions and the use of firm- and household- level data. While each of the different methodologies and aggregation levels has its shortcomings, the body of evidence accumulated over the past 15 years provides a strong case for a relationship between financial development and economic growth that is not driven by omitted variables, measurement error or reverse causation.

While the profession has made great progress in measuring financial development, especially by moving towards micro-data, this paper has focused on methodological advances to overcome the biases illustrated by a simple cross-country OLS regression. Most importantly, overcoming endogeneity and simultaneity biases with a proper identification strategy has been the main challenge for researchers. While the crosscountry literature has focused on finding external and internal instruments, the time-series literature has exploited high-frequency data, a rich lag structure, and the forecast capacity of finance for GDP per capita. Differences-in-differences approaches address the identification challenge by assessing natural experiments, exploiting either exogenous policy reforms or inherent industry characteristics that result in a differential impact of financial development.

Using firm- and household-level data allows a deeper look into the mechanisms through which finance enhances firm growth and household welfare and thus provides additional evidence, but poses its own set of identification challenges. While many of the methodologies used at the cross-country-level, such as instrumental variables or differences-in-differences, can also be applied at the firm- and household-level, randomized controlled experiments with households and microentrepreneurs open new and exciting research opportunities, as they allow researchers to subject households and microenterprises to a specific treatment under the control of the researcher.

32
As the discussion of the comparative costs and benefits of subjective indicators vis-àvis their objective counterparts suggests, the costs and benefits of each type of indicator may act as a check on the weaknesses of the other type of indicator. Specifically, subjective indicators may be able to measure broader types of corruption, but then it should be the case that, if they are valid, they will be highly correlated with narrower, objective indicators of corruption. Unsurprisingly, given this observation, and despite the continued and lively debate in the literature (see Tresiman, 2007, for a review), there is a growing consensus that both types of indicators can be valid, consistent, and can provide cross checks for each other. This is because a growing number of research projects have found that, ordinarily, objective and subjective indicators of corruption usually correlate quite strongly with each other, especially if the standard errors of both are taken into account (Kaufmann et al, 2007). Thus, initial skepticism towards subjective indicators is misplaced (e.g. Olken (2009).

# **_II. Operationalizing Corruption: The Different Indicators_**

From the universe of valid and consistent measures of corruption, it is now essential to critically review which one of these indicators is most likely to capture the type of ‘corruption that is of theoretical interest. Two indicators emerge as most likely to enjoy a comparative advantage in measuring broad corruption: the CPI and the CC.

## **(A) Measuring Corruption: Survey‐Based Indicators**

Since the mid-1990s there has been an exponential growth in the number of indictors attempting to measures corruption (see Treisman, 2007, for a review). Of particular importance has been the development of ‘composite survey based indicators’ of corruption, which try to utilize multiple (survey) sources to increase the accuracy of their measures. In accordance with most of the literature (e.g. Persson and Tabellini, 8
service provision in separate models due to high correlations among the various indicators.<sup>11</sup> Figure 7 illustrates the effect of donor and non-state actor service provision on citizens’ willingness to defer to the tax department. With one exception, analyses point to a positive relationship between donor and non-state actor service provision and the willingness to defer to the tax department. Respondents who believe that traditional actors are responsible for collecting taxes are less likely to be willing to accept the authority of the tax department than respondents who believe that the government is responsible for collecting taxes.

I expected respondents who believe that non-state actors, rather than the state, are responsible for collecting taxes to be less likely to be willing to defer to the tax department. The results suggest that this is the case. A belief that traditional leaders instead of the central or local government are responsible for collecting taxes corresponds to a percentage point increase of 5.8 that one will be unwilling to defer to the tax department.

I argue that non-state actor and donor service provision is likely to undermine the fiscal contract between a state and citizens by providing citizens with essential goods and services that they believe the state has an obligation to provide. The analysis of the Afrobarometer data does not yield support for this hypothesis. Instead, I find a positive relationship between donor and non-state service provision and deference to the tax department. In other words, citizens who believe that donors and non-state actors are helping their country by delivering essential goods and services are more likely to be willing to defer to the tax department. This positive relationship is consistent across all of my measures of donor and non-state actor service provision.<sup>12</sup> Of the indicators of donor and non-state actor service provision, the measure of service provision by the UN has the largest relative effect on the acceptance of the tax department’s right to make people pay taxes. A belief that the UN is doing a lot, rather than a little, to help one’s country corresponds to a percentage point increase of 8.4 in the predicted probability of deferring to the tax department. A belief that international donors and NGOs are doing a lot to help one’s country corresponds to a percentage point increase of 7.2 in the predicted probability of deferring to the tax department.

> 11These regression tables are available upon request.

> 12Using different survey data collected in Zambia, I find a negative association between donor and non-state actor service provision and reported compliance. With the exception of church provision of welfare, I do not find any association between donor and non-state actors service provision and legitimating beliefs.

22
# _2.1 Capital regulation_

The GFC highlighted the risk of having thin capital buffers to cover unexpected losses. Across the world there has been a trend toward increasing the minimum regulatory capital requirements to improve banking system resilience. The increase in the minimum regulatory capital requirement has translated into higher levels of actual holdings of regulatory capital. This is particularly true for high-income OECD countries where capital holdings increased from a mean value of 14.6% of risk-weighted assets (RWA) in 2010 to 18.7% of RWA in 2016 (Figure 2, Panel A). Conversely, we do not observe statistically significant differences in the average levels of regulatory capital holdings in developing countries between 2010 and 2016, possibly because they already had higher levels of capital to start with. Nevertheless, except in South Asia and in ECA, holdings of regulatory capital in developing regions ended up lower than in high-income OECD countries. Moreover, in ECA and MENA we observe a modest decrease in regulatory capital holdings. None of these differences are statistically significant however, suggesting that regulatory capital has not changed much in developing countries.

Another contentious issue brought to the fore of the policy discussion after the GFC was the computation of capital requirements. The adequacy of capital to cover unexpected losses can be achieved by increasing the level and/or quality of regulatory capital or by decreasing total risk exposure (Jones, 2000). The GFC highlighted the crucial importance of defining bank capital narrowly to improve bank performance, especially in times of crisis, and high-loss absorbency components such as common equity should be preferred. Data from the 2019 BRSS shows that levels of Tier 1 capital, the regulatory capital component with the highest capacity for loss absorption, increased significantly over time for high-income OECD countries (5 percentage points), but did not change much for developing countries. Looking at individual regions reveals some positive (East Asia and South Asia) and negative (ECA and MENA) changes, but again none of these are statistically significant (Figure 2, Panel B).

6
# **Table 8:  Regression Results: Electrification Rate (as % of population)**

||Electrification|Rate (as % of population)|||
|---|---|---|---|---|
|Procedures Getting Electricity|-0.791|-0.793|0.996|0.993|
||(2.568)|(2.588)|(1.917)|(1.926)|
|Private utility||<br>1.581||<br>3.271|
|||(8.057)||(5.503)|
|Ln GNI per capita|||19.362***<br>|<br>19.393***<br>|
||||(1.44)|(1.43)|
|Constant|71.002***|70.621***|<br>-90.646***|<br>-91.687***|
||(15.135)|(15.238)|(16.611)|(16.485)|
|R-squared|0.001|0.001|0.612|0.614|
|<br>Observations|84|84|84|84|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||
||Electrification|Rate (as % of population)|||
|Time Getting Electricity (Log)|<br>-21.030***|<br>-21.665***|-7.335|-7.098|
||(5.107)|(5.239)|(3.843)|(4.112)|
|Private utility||<br>-4.134||<br>1.323|
|||(7.655)||(5.716)|
|Ln GNI per capita|||18.273***<br>|<br>18.317***<br>|
||||(1.55)|(1.57)|
|Constant|159.813***|163.656***|<br>-44.177|<br>-45.905|
||(22.514)|(23.587)|(24.925)|(26.684)|
|R-squared|0.132|0.135|0.625|0.625|
|<br>Observations|84|84|84|84|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||
||Electrification|Rate (as % of population)|||
|Cost as % of GNI pc|-12.342***|-12.340***|-2.395|-2.297|
|<br>Getting Electricity (Log)|(1.042)|(1.045)|(2.151)|(2.210)|
|<br>Pi ili||<br>1119||<br>2938|
|rvate utty||.<br>(5900)||.<br>(5596)|
|Ln GNI per capita||.|16.253***|.<br>16.404***|
||||(3.33)|(3.41)|
|Constant|148.059***|147.765***|<br>-45.02|<br>-47.585|
||<br>(6.726)|<br>(6.963)|<br>(38.900)|<br>(40.304)|
|R-squared|0.516|0.516|0.615|0.617|
|<br>Observations|84|84|84|84|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||
||<br>Electrification|<br>Rate (as % of population)|||
|Country rank on the ease of getting|-0.329***|-0.331***|-0.062|-0.058|
|<br>electricity|(0.051)|(0.049)|(0.055)|(0.057)|
|Private utility||-2014||2532|
|||.<br>(7132)||.<br>(5565)|
|L GNI it||.|17888***|.<br>17989***|
|n per capa|||.<br>(201)|.<br>(204)|
|Constant|98.245***|98.896***|.<br>-67.735***|.<br>-69.493***|
||(4.737)|(4.546)|(19.577)|(20.064)|
|R-squared|0.267|0.268|0.617|0.618|
|<br>Observations|84|84|84|84|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||

39
# **Table 5: Economic Growth and Billionaire Net Worth using Disaggregate Price Shocks as Instruments**

|**Outcome Variable**||**Billionaire**|**Net Worth**||
|---|---|---|---|---|
|**Model**|**IV Country a**|**nd Year FE**|**IV Billionair**|**e and Year FE**|
||**Second Stage**|**First Stage**|**Second**<br>**Stage **|**First Stage**|
|**Outcome Variable**|**Billionaire Net**<br>**Worth**|**Log**<br>**difference**<br>**GDP**|**Billionaire**<br>**Net Worth**|**Log**<br>**difference**<br>**GDP**|
||**(3)**|**(4)**|**(1)**|**(2)**|
|Log difference GDP|3.282***<br>(0.944)||4.088***<br>(0.837)||
|Log Difference of Hydrocarbons Commodity Index||0.052***<br>(0.004)||0.054***<br>(0.005)|
|Log Difference of Metals and Minerals Commodity Index||-0.041***<br>(0.013)||-0.045***<br>(0.013)|
|Log Difference of Agriculture, Food, and Beverages Commodity<br>Index||-0.176***<br>(0.019)||-0.161***<br>(0.020)|
|Underidentification test (p-value)|0.000||0.000||
|Weak identification test (F stat)|76.063||62.295||
|**Stock-Yogo weak ID test critical values:**|||||
|5% maximal IV relative bias|13.910||13.910||
|10% maximal IV relative bias|9.080||9.080||
|20% maximal IV relative bias|6.460||6.460||
|30% maximal IV relative bias|5.390||5.390||
|10% maximal IV size|22.300||22.300||
|15% maximal IV size|12.830||12.830||
|20% maximal IV size|9.540||9.540||
|25% maximal IV size|7.800||7.800||
|Hansen J statistic (overidentification test, p-value)|0.040||0.253||
|Billionaire Fixed Effects|NO|NO|YES|YES|
|Country Fixed Effects|YES|YES|NO|NO|
|Year Fixed Effects|YES|YES|YES|YES|
|Number of observations|14,431|14,431|13,835|13,835|

note: *** p<0.01, ** p<0.05, * p<0.1, robust standard errors clustered at the billionaire level 17
The short rainfall insurance education module administered in Andhra Pradesh has no significant effect on insurance demand. While this lack of a response may reflect the specific content of this particular education intervention, Cole, Thomas Sampson, and Bilal Zia (forthcoming) find in Indonesia that a significantly more involved financial education program also has little effect on financial decision-making.

# _E. Framing, salience and other behavioral factors_

We find only limited evidence that pure framing effects identified in the psychology and behavioral economics literatures significantly affect rainfall insurance demand. Specifically, there are no significant differences in take-up amongst eight different frames of the rainfall insurance used in the Gujarat video experiments. While in some cases our power to reject the null is limited, a two standard deviation confidence interval for each individual framing treatment is generally no larger than ± 6 percentage points, and in nearly every case we can reject the null that frame shifts demand by more than 10 percentage points.

These results appear significantly weaker than Bertrand et al. (2010), who find that framing has large effects on credit demand in a large field experiment in South Africa. Our results also stand in contrast to laboratory experiments by Eric Johnson et al. (1993) and Mittal and Ross (1998), which find framing effects to be important determinants of (hypothetical) demand for insurance. One interpretation of these differences is that the impact of framing effects is likely to be heavily context-specific, and thus may vary significantly across different studies.

We do find in Andhra Pradesh that being assigned a door-to-door household visit significantly increases insurance take-up, even when not combined with other treatments. This result obtains even though the product is readily available to all village households. This may reflect the added convenience of being able to purchase insurance “on-the-spot,” or be due to the effect of the baseline information provided by the ICRISAT insurance educator. Alternatively, the household visit may simply make the insurance product more salient to the household, which in a model of limited attention (e.g. Reis, 2006) would be expected to influence demand.

# **V. Non-experimental evidence**

20
investors are reluctant to invest in the Energy and Basic Materials’ corporate bonds, even when they are of higher ratings. The sectors whose investment grade bonds benefit the most are relatively less cyclical: Utilities, Industrials, Healthcare and Communications. For a 10% decline in the WTI oil price, the indices for these sectors’ investment-grade bond indices increase from 0.32% to 0.56%.

# **V. Impact on Emerging Markets**

This section considers the impact of oil price declines to emerging markets. Table 5.1 shows that oil price declines hurt the dollar-denominated MSCI Emerging market index. For every 10% decline in WTI oil price, the dollar value of MSCI Emerging Market index drops by 1.32%. The MSCI Emerging Markets Index consists of 21 emerging countries,<sup>15</sup> most of them are not oil exporters. Nevertheless, we still see the declines in these markets. This reinforces the concern that this time, lower oil price could carry global systemic risk.

Table 5.1 MSCI Emerging Market index

|VARIABLES<br>∆log (MSCI<br>Emerging)|∆log (MSCI<br>Emerging)|
|---|---|
|∆_Log( Oil Price)_<br>0.142**<br>(0.0709)|0.132**<br>(0.0534)|
|Lags<br>No|Yes|
|Constant<br>9.41e-05|0.000181|
|(0.000358)|(0.000339)|
|Observations<br>699|696|
|R-squared<br>0.068|0.168|
|Robust standard errors in parentheses<br>*** p<0.01, ** p<0.05, * p<0.1||

> 15 <mark>Brazil; Chile; China; Colombia; Czech Republic; Arab Republic of Egypt; Hungary; India; Indonesia; Republic of Korea; Malaysia; Mexico; Morocco; Peru;  Philippines; Poland; Russian Federation; South Africa; Taiwan, China; Thailand; and Turkey.</mark>

23
the rise and has taken over the bilateral trade between India and USA since 2008. Another interesting thing to note is that since the onset of the crisis, trade between India and China has held up while between India and USA has shown a declining trend. This is clear cut evidence that South-South trade is far more important now and has recovered much faster than South-North trade.

<!-- Start of picture text --> Figure 11: Share of Pakistan’s Export with South and USA<br>30<br>25<br>20<br>15<br>10<br>Share of Exports to UAE and Afganistan<br>5<br>Share of Exports to USA<br>0<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> _Source_ : Economist Intelligence Unit database and authors’ calculations.

There is similar evidence from other South Asian countries. Share of exports from Pakistan to UAE plus Afghanistan has surpassed the share of exports from Pakistan to USA (Figure 11). It is also worth noting that all the top four importing countries to Pakistan are developing countries or Middle Eastern countries. Similarly for Bangladesh and Sri Lanka, China and India are the two most important trading partners accounting for 30% of their total imports for both the countries.

Decoupling of trade for South Asia only means that for its own recovery, the region is not fully dependent on the developed economies recovery. South Asia is not yet as globally integrated as other regions. This crisis provides an opportunity to open its doors not only to the rest of the world but also to its neighbors. Increased South-South trade in the region could add two percentage points more to GDP growth and enable the land locked regions in Afghanistan, Pakistan, Nepal, Bangladesh and lagging regions of India to transform globalization into inclusive growth.

Thus we see the unique and distinctive growth and trade patterns of South Asia are going to help rather than hinder the recovery. Its high quality service exports led growth is likely to help it recover quickly and sustain its high growth in the medium run.

22