rafmacalaba/datause-encoder-data huggingface dataset:
**- -** **<u>Table 3. The Effect of Payment Systems Reform of Credit; Robustness to Lag Length, Sub Sample Variations and Control Experiment</u>**

**The dependent variable in all three panels is the Credit/GDP ratio**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|||||_Control Experiment:_<br>|
||_two lags_<br>_excluding non-_<br>_reform countries_|_four lags on_<br>_control_<br>_variables_|_No Lag_|_Trend break for non-_<br>_Adopters at the average_<br>_reform date_|
|**Post-Reform Trend Break**|**0.038****|**0.049****|**0.040***|**-0.010**|
||**(0.016)**|**(0.016)**|**(0.019)**|**(0.011)**|
|Inflation|0.006**|0.0002|0.002|0.005|
||(0.002)|(0.002)|(0.003)|(0.008)|
|Inflation*(High Inflation dummy)|-0.005*|-0.0005|-0.00005|0.0003|
||(0.002)|(0.002)|(0.003)|(0.009)|
|High Inflation dummy|0.030|-0.002|-0.015|-0.086|
||(0.019)|(0.02)|(0.032)|(0.055)|
|Expenditure over GDP ratio|-0.998|0.140|-1.364**|-1.632|
||(0.740)|(0.520)|(0.578)|(1.111)|
|Log real GDP per capita|1.258***|-0.048|0.085|0.488*|
||(0.203)|(0.164)|(0.183)|(0.169)|
|Banking reform index|-0.791*|-0.566**|-0.821|-0.137|
||(0.354)|(0.248)|(0.663)|(0.357)|
|Banking crises|0.087|0.067|0.159|-0.101|
||(0.076)|(0.073)|(0.107)|(0.112)|
|Public Banks' Assets Share|0.006|0.003|0.007*|0.007|
||(0.004)|(0.003)|(0.003)|(0.006)|
|Foreign Banks' Assets Share|-0.0007|-0.002|-0.0001|-0.002|
||(0.003)|(0.002)|(0.003)|(0.004)|
|Foreign Exchange reserves/GDP|0.009***|0.015***|0.011***|0.595|
||(0.002)|(0.002)|(0.002)|(0.495)|
|Country specific trends|x|x|x|x|
|Year effects|x|x|x|x|
|Country effects|x|x|x|x|
|Seasonal Dummies|x|x|x|x|
|Within R-squared|0.89|0.88|0.85|0.97|
|Nbr. obser.|288|380|420|112|

Note: Robust clustered standard errors in parentheses. Significant at (*) 10 per cent level; (**) 5 per cent level; (***) 1 per cent level.
relationship for services diversity. These correlations are also evident when using an alternative definition of modern services that classifies firms as modern if they had high ICT use (using data from the 2010-11 NSS survey).

# **5. Conclusions**

India’s economic geography is a very lively research topic, full of many nuances and twists befitting the rapidly developing country and its present and past policy frameworks. This study simultaneously resolves questions on some dimensions and opens up questions on other dimensions. In terms of what it resolves, the prior study by Ghani, Kerr and Tewari (2013) found some interesting patterns with respect to manufacturing specialization and diversification. Yet, strong conclusions were not forthcoming given the exclusive focus on one sector. By shortening the time frame and incorporating additional surveys, the present study provides a more secure base. It confirms the comparable specialization and diversity levels to what is observed in the United States, the trend for convergence and then modest re-expansion of the distributions, the relative roles of modern and traditional industries, and so on.

On the other hand, we have several findings that deserve further attention. First, we have yet to find a consistent link in India between the size or density of districts and their diversity patterns. This was true in our earlier work for manufacturing, and it continues to hold with services and our joint analyses. This pattern is different than what is observed in other places like Colombia and the United States. It is worth further investigation as to the source of this deviation. Such an extension would also allow us to compare India more closely with the work on Colombia by Duranton (2013b), who finds differences between manufacturing and services performance around specialization dynamics. This added perspective will provide a richer foundation for understanding growth in Indian cities. Detailed case studies around the growth of modern services in the extreme cases identified of initial employment are also warranted.

Second, and perhaps even more intriguing, is our connection of diversity to employment growth in rural areas of districts and districts with low population density. While we find a broad and robust connection of diversity to local employment growth in many settings, the fact that the sharpest expressions occur in these unexpected places is surprising to us. Beyond the standard 23
- Pogge, T. and Wisor, S. (2016). ‘Measuring Poverty: A Proposal’, In: M. Adler and M. Fleurbaey (eds.), _The Oxford Handbook of Well-Being and Public Policy_ . Oxford: Oxford University Press.

- Roelen, K. (2018). ‘Poor children in rich households and vice versa: A blurred picture or hidden realities?’, _European Journal of Development Research_ , 30, pp. 320–341.

- Roelen, K., Gassmann, F., and de Neubourg, C. (2010). ‘Child poverty in Vietnam: Providing insights using a country-specific and multidimensional model’, _Social Indicators Research_ , 98(1), pp. 129–145.

- Rogan, M. (2016). Gender and Multidimensional Poverty in South Africa: Applying the Global Multidimensional Poverty Index (MPI). _Social Indicators Research,_ 126: p.987-1006.

- Scott, J., Crompton, R. and Lyonnette C. (eds) (2010). _Gender Inequalities in the 21st Century_ . Cheltenham: Edward Elgar.

Sen, A. (2009). _The Idea of Justice_ . Cambridge, Mass.: Belknap.

- South African Human Rights Commission (SAHRC) and UNICEF. (2014). _Poverty Traps and Social Exclusion among Children in South Africa 2014_ . Pretoria: SAHRC and UNICEF.

- Trani, J.-F. and Cannings, T. I. (2013). ‘Child poverty in an emergency and conflict context: A multidimensional profile and an identification of the poorest children in Western Darfur’, _World Development_ , 48(C), pp. 48–70.

- UNDP. (2014). _UNDP’s Multidimensional Poverty Index: 2014 Specifications_ . New York: Human Development Report Office.

- UNHCR. (2020). Ethiopia country refugee response. Retrieved from https://reliefweb.int/report/ethiopia/ethiopia-country-refugee-response-plan2020-2021.

- Ura, K., Alkire, S., Zangmo, T. and Wangdi, K. (2012). _An Extensive Analysis of the Gross National Happiness Index_ . Thimphu: Centre of Bhutan Studies.

33
further confirmed by looking at the main occupations in the agriculture sector for the two groups: In the United States, half of the workers (47%) are "Agricultural and forestry production managers", while in the LICs, half of the workers are employed as "Subsistence mixed crop and livestock farmers" (31%) and "Crop farm laborers" (16%). In sum, we recognize the caveat that the AIOE results for LMICs are likely to provide an upper bound, but the change in occupations and AIOE across income groups gives us confidence in our approach.

To summarize the characteristics associated with AI occupation exposure, we pool data from the latest survey for each country and apply the following simple regression model at the level of the individual worker with AIOE as the independent variable: = X tt,cc,ii tt,cc,ii tt,cc,ii yy xx + εε Where _i_ is worker _i_ in year _t_ and country _c_ , and is the AI occupation exposure for that worker. The matrix _X_ has dimension _n_ x _p_ , where _n_ refers to the number of workers and _p_ to the variables. tt,cc,ii yy The variables include 1-digit ISCO occupation information, sector, gender, region of residence, employment status, age group, survey year, and country. All results use individual weights to ensure country representativeness. Robust standard errors are clustered at the level of country (c) and survey year (t), and all analyses are restricted to workers aged 15-64. The role of the regression is to describe the descriptive relationship between the AIOE and the independent variables in a multivariate framework. Its values are of a correlational nature and so is its interpretation.

For bivariate representations we divide the values of AIOE in quartiles with cuts at 23, 35, 51 and 82: “Low exposure”, “Moderate low exposure”, “Moderate high exposure”, “High exposure”. The sample consists of 4 low-income countries, 12 lower-middle-income countries, 7 upper-middleincome countries, and 2 high-income countries as per World Bank country income classifications.

10
the first table presume that the climate variable is precipitation; for other climate variables substitute the relevant unit wherever ‘mm’ appear. The ‘file type’ given in the first table is a backwards reference to the ‘ingredients’ table, Table 5b.

# _Procedure_

The unpacking of the raw files into the scenario dataset comprises: employing the above equation to combine the information in the raw files into scenario data, and checking that the scenario data lie within the permissible range.

The sequence of operations that might be performed in carrying out the unpacking is summarized below. This is given as an example and is not prescriptive: the user is at liberty to use any sequence that satisfies the above equation. As an example we show how to obtain, for the 0.5° grid-box (363, 286) containing Norwich (52° 38' N, 1° 19' E), the projection of precipitation in July 2081 under the scenario given by using the climate model HadCM3 to represent the A2 emissions scenario. Convert the response pattern into a normalized response pattern ( _p_ ).

Example: the change in precipitation in 2070–2099, relative to 1961–1990, is -23.9 mm; the amount of global warming, i.e. 3.931°C, is used to convert this into the change in precipitation per degree of global warming, i.e. -6.1 mm °C<sup>-1</sup> . Obtain the time series of local climate change ( _p * t_ ); this varies only at century time scales.

Example: the normalized pattern, i.e. -6.1 mm °C<sup>-1</sup> , is multiplied by the amount of global warming in 2081 under the HadCM3 A2 scenario, i.e. 3.734°C<sup>-1</sup> , to give an anomaly of -22.7 mm.

Add the interannual and multidecadal variability ( _o'_ ) derived from the observed period (1901–2000). Example: the long-term change of -22.7 mm is modified by the residual (from the ‘residual’ file: iavar.1901–2000.pre) for July 1981, i.e. +16.3 mm, to give a combined anomaly of -6.4 mm.

Add the mean climate ( _o_ ) from the observed period (1961–1990). Example, the combined anomaly of -6.4 mm is added to the climatological mean of 56.9 mm to give a combined precipitation of 50.5 mm.

19
narrow the gap by providing itemized estimates of potential damage and adaptation cost out to 2050 for Bangladesh‐ a tropical cyclone hotspot<sup>14</sup> in a changing climate.

The focus of this research is on 10‐year return period cyclones ‐‐ cyclones with a 10% or greater probability of occurrence each year. In the computation of potential damage, this analysis concentrates on fatalities and injuries; and on housing, education, agriculture, non‐ agriculture productive sector, and infrastructure (roads, power, coastal protection), sectors experiencing serious damage in the most recent 10 year return period cyclone in Bangladesh (Sidr 2007). The primary objective of adaptation in this analysis is to avoid further damages associated with storm surge inundation as a result of climate change with a particular focus on various measures of adaptation to storm surges in Bangladesh. At the outset, we acknowledge several limitations in our analysis. This analysis has not addressed the likely problem of salinity intrusion nor has it addressed out‐migration from the coastal zone that may be induced by sea level rise and more intense cyclones. We also have not attempted to estimate location‐specific probability of 10‐year return period cyclones.

The remainder of the paper is organized as follows. Section 2 presents the case of cyclones in Bangladesh. Our methodology and results are presented in Section 3, and Section 4 concludes the paper.

# **2. Cyclones in Bangladesh**

Cyclones hit the coastal regions of Bangladesh almost every year, in early summer (April‐ May) or late rainy season (October‐November). Between 1877 and 1995 Bangladesh was hit by 154 cyclones (including 43 severe cyclonic storms, 43 cyclonic storms, 68 tropical depressions)<sup>15</sup> . Since 1995, five severe cyclones hit coast of Bangladesh coast in May 1997, September 1997, May 1998, November 2007 and May 2009. On average, a severe cyclone strikes Bangladesh every three years (GoB, 2009).

Bangladesh is on the receiving end of about 40% of the impact of total storm surges in the

> 14 See UNDP, 2004; Nicholls, 2003 .

> 15 The Indian Meteorological Department (IMD) is responsible for tracking tropical storms and cyclones in South Asia including the Bay of Bengal.  Tropical storms are classified based on the observed maximum sustained surface wind measured at a height of 10m averaged over 3 minutes as follows:  Super Cyclonic Storm (greater than 220 km/hour), very severe cyclonic storm (119‐220 km /hour), severe cyclonic storms (90‐119 km/hour), cyclonic storms (60‐90 km/hour), Deep depression (51‐59 km/hour), Depression (32‐50 km/hour) (IMD, 2010).
# **Panel A. Number of new market participants**

<!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Bottom quartile Experience Rate<br>Top Quartile Experience Rate<br><!-- End of picture text -->

# **Panel B. Share of new market participants over inexperienced students**

<!-- Start of picture text --> 0.4<br>0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Bottom Quartile Experience Rate<br>Top Quartile Experience Rate<br><!-- End of picture text -->

# **Figure 4**

# **Market participation**

The graphs plot the average number of new market participants per course (Panel A) and the corresponding percentage, normalized by the number of inexperienced students in each group (Panel B). Solid and dashed lines compare market participation across courses in the top and bottom quartile sorted by Experience Rate (i.e., share of students with trading background in the course).

53
53 been running at about 200-300 per month, but after recent U.S.-Vietnam discussions, the State Department feels that the number could increase to 1,000 new arrivals per month.

For Sri Lanka, I have trouble reconciling the United Nation's estimate of -300,000 for 1980-1985 with the previous World Bank estimate of -60,000. I do not have data for the entire period, but the figures that I have show net emigration of -68,000 for 1980 and -50,000 for 1981. One possibility is that the United Nations is considering refugees from Sri Lansa who are currently in India (about 125,000 according to the U.S. Committee for Refugees). Nevertheless, the previous World Bank figure seems too low. I would suggest a figure of -125,000 for 1980-1985.

Finally, the sex ratios for net immigration from some countries (in particular India, Taiwan, and Vietnam) seem too high in the most recent World Bank projections. Information on the sex of immigrants from those countries to the United States for fiscal years 1982-1987 is shown in Table 21. There is only a very slight preponderance of males from India. The sex ratio for the six-year period is 102.3. While Indian emigration to the United States is only a minority of all Indian emigration, this would imply that the sex ratio of Indian emigration to countries other than the United States would be almost 300, which seems much too high. Moreover, the previous World Bank estimates have the sex ratio increasing slightly over time, which also seems unlikely. A sex ratio of around 130 in the early 1980s followed by a declining sex ratio over time might make more sense. For Taiwan, there are more females than males migrating to the United States. The sex ratio of Taiwan migrants to the United States was 88.0 for the period 1982-1987. Therefore, once again the previously estimated World Bank sex ratio seems too high. Perhaps an even distribution between the sexes would be more reasonable. For Vietnam, the overall sex ratio of 144.4 for immigrants to the United States for 1982-1987 is close to the previous World Bank figure of 150 for each five-year period. However, the sex ratio has been dropping (to 136 in FY1985, 130 in FY1986, and 122 in FY1987). The data seem to indicate a trend toward lower sex ratios for more recent years. Therefore, I would recommend a slightly lower sex ratio of about 140 for net emigration from Vietnam for 1980-1985 and 130 for each subsequent five-year period.

Table 21. Immigrants to the United States from India, Taiwan and Vietnam

||I|ndia|Tai|wan|Vi|etnam|
|---|---|---|---|---|---|---|
|Fiscal Year|Male|Female|<br>Male|Female|Male|<br>Female|
|1982|10,452|<br>10,487|<br>4,678|5,200|41,758|<br>30,533|
|1983|12,420|<br>11,904|<br>7,722|8,747|22,997|<br>14,372|
|1984|12,655|<br>12,309|<br>5,880|6,598|37,236|<br>22,036|
|1985|13,330|<br>12,696|<br>6,905|'|1R197|<br>13 '9°|
|1986|13,248|<br>12,979|<br>6 2}||||
|1987|13,907|<br>13,896|<br>5,53'|.7<br>,<br>J7|||
|1982-87|76,012|<br>74,271|37,002|42,073|150,676|104,375|

Source: Immigration and Naturalization Service, Statistical Yearbooks, various years.
**Table 3 Firm level Reported Import Values, Quantities and Prices – by Source-Origin**

||**M**<br>_Normaliz_<br>|**ean Import Va**<br>_ed by product-or_<br> _(product-origin_<br>|**lues, Quanti**<br>_igin-year-ind_<br>_-year-industr_<br>|**ties, and Prices**<br>_ustry weighted av_<br>_y average=1)_<br>|_erage_<br>||
|---|---|---|---|---|---|---|
|**Firm Type**|**_Ben Ali_**<br>(N=8140)|**_Offshore_**<br>(N=15880)|**_Onshore_**<br>(N=23775)|**_Public_**<br>(N=237)|**_A_**<br>(N=4|**_ll_**<br>8032)|
||_Mea_|_n Import Value_|||_Std.Dev._|_N_|
|_All_|1.183|1.030|0.934|1.403|1.383|48032|
|High Tariffs|1.214|1.032|0.923|1.458|1.440|33147|
|Low Tariffs|1.131|1.020|0.955|1.355|1.246|14885|
||_Mean_|_Import Quantit_|_y_||_Std.Dev_|_N_|
|_All_|1.205|1.009|0.923|1.340|1.466|48032|
|High Tariffs|1.253|1.010|0.911|1.414|1.518|33147|
|Low Tariffs|1.124|1.007|0.960|1.273|1.342|14885|
||_Me_|_an Unit Prices_|||_Std.Dev_|_N_|
|_All_|0.952|1.022|1.014|1.086|0.791|48032|
|High Tariffs|0.919|1.027|1.023|1.056|0.764|33147|
|Low Tariffs|1.008|1.000|1.000|1.114|0.851|14885|

Note: sample is confined to (5-digit) sector-product-source combinations in which (i)at least one Ben Ali firm and at least one other firm of a different type are simultaneously importing during the same year, (ii) for which tariff data exist,(iii) in which imports reported in Tunisia and exports reported in partner countries are strictly positive, (iv) from countries which are among the top 15 source countries in terms of cumulative import value (either as declared by partners or as declared by Tunisian customs) over the period 2002-2009, and (v) products which account for at least 0.01 percentage points of import value (either as declared by partners or as declared by Tunisian customs) over this period. In addition, (vi) observations that fall in the top and bottom 1% of normalized prices, quantities and values are excluded. The sample comprises 9 public firms, 1787 onshore firms, 1145 offshore firms, and 113 BA firms.

40
**Table 10: Estimate of average WTP in Europe, relationship to own-country average income. US$ increase in WTP per US$10,000 average own-country per-capita income; with income as the only variable (raw) and with background variables included , see table 12 (corrected)**

||Roun|d 1|Roun|d 2|
|---|---|---|---|---|
||WTP change<br>(per 10,000<br>euros, own<br>country<br>income)|t statistic|WTP change<br>(per 10,000<br>euros, own<br>country<br>income)|t statistic|
|WTP plan A (raw variable)|-1.99|-1.21|-0.66|-0.65|
|WTP plan A (corrected)|-2.97|-1.59|-1.21|-1.11|
|WTP plan B (raw)|-1.55|-1.10|-0.58|-0.74|
|WTP plan B (corrected)|-2.47|-1.61|-0.85|-1.06|

# **4.3 Which Factors Affect Experts’ WTP Estimates, and How?**

Data on experts ́ experience in conducting SP studies (both CV and CE) , multi-country studies, benefit transfer exercises and their assessment of how close the Delphi estimate would be an actual CV study were collected in Round 1 ( see appendix 1 for the questionnaire), and regressed on their stated WTP amounts in Round 1 and Round 2. The variables are described in table 11, and the results presented in tables 12 and 13.

18
through both ex-ante strategies, such as insurance as well as ex-post financing, for example through loans. However, for developing countries, it can often be difficult to get access to external loans through private markets, leaving only insurance, external aid, tax increases or internal redistribution of finances as potential sources of funding (Bevan _et al._ , 2016; Mahul & Ghesquiere, 2010). With insurance still uncommon in most developing countries<sup>2</sup> , foreign aid is usually available only after large disasters, and tax increases politically unpalatable, redistribution of spending across different budget categories is perhaps the only remaining or default alternative for post-disaster financing for many governments. A key question - that will be addressed in this paper - is which sectors receive less funding to finance the disaster relief. This is an insight that can potentially help policy makers detect any sectors that experience shortfalls following disasters.

Budget reallocation as a response to post-disaster financing has received only scarce attention in the literature. For example, papers such as Bevan _et al._ (2016) focus on the redistribution at a sovereign level, and only as a theoretical exercise. In this paper we will use detailed budget expenditure data for Indonesian districts for the period 2005 through 2012, and combine these with spatially disaggregated damage indices for floods, earthquakes, volcanic eruptions and the 2004 tsunami to construct a unique spatial panel data set. This will allow us to obtain a first empirical estimate of how local governments change their spending following natural disasters.

The district level budget data contain revenues and expenditures provided every year to

> 2Rauch & Neuthor (2013), for example, claim that for the years 1980-2012, low income countries constituted 10 percent of disaster losses, but only 1 percent of the insured losses.

3
19 Estimation results of the Davoodi and Zou model using indicators of Ebel and Yilmaz also appear in the Panel A of Table 5. In the Ebel and Yilmaz study, tax autonomy and non-tax autonomy both had a significant and positive relation with per capita GDP growth, and tax sharing had a significant negative one. Except for tax autonomy, which is not significant here, results of simple regressions reported on the right side of Panel A are similar to the ones of Ebel and Yilmaz. However, when controlling for other variables’ effects, the tax sharing coefficient looses its significance. Non-tax revenue still has a positive and significant coefficient and fiscal dependency now gets a negative and significant one (see also Table 6).

_Table 6 :_ **_Sign and significance of estimated coefficients, decentralization variables of the Davoodi and Zou Model_**

|_Variables_|**Per capita G**|**DP growth**|
|---|---|---|
||**Multiple**<br>**regression**|**Simple**<br>**regression**|
|Subnational spending<br>rationetofgrants|(+)|(-)|
|Subnational tax<br>autonomy|(-)|(-)|
|Subnational tax<br>sharing|(+)|(-)**|
|Subnational fiscal<br>dependency|(-)***|(+)|
|Subnational non-tax<br>autonomy|(+)***|(+)***|
|Subnational revenue<br>autonomy|(+)***|(+)*|
|Subnational own<br>revenueratio|(+)|(+)***|
|Subnational dependent<br>revenue ratio|(-)|(-)*|

- Significant at the 1 % level. **Significant at the 5 % level. ***Significant at the 10 % level.

The most interesting results here are the one presented in Panel B of Table 5. Estimation results of multiple regressions, on the left side, show a positive and significant coefficient for the subnational revenue autonomy variable and no significant coefficient for the two other variables. This implies that, even if subnational share of governments’ revenue has no significant impact on economic growth, the composition of revenue has a significant impact on it. Other things being equal, economies with a higher level of subnational