rafmacalaba/datause-encoder-data huggingface dataset:
accounts that their employees open. Also, the new banking structures with activities done both centrally at headquarters and at the branch level make it difficult to estimate unit cost accounting properly. Moreover, because new financing products are being offered with different types of risk-mitigating guarantees but at relatively low interest spreads to gain market share, it is difficult to assess the overall risks that this new engagement entails, especially considering that recent default rates are deceivingly low inasmuch as they reflect an unusually benign environment.

Fourth, future research could fruitfully analyze the consequences on the banking sector, including on its structure, stemming from the new ways in which banks are interacting with SMEs. For example, if it is true that universal banks have an advantage in serving SMEs (including lending to them), the viability of small, niche banks specialized in SMEs would be in question.

Fifth, the application of questionnaire-based surveys (such as those documented in this paper) to other countries would help identify policy priorities appropriately tailored to individual countries, on the one hand, and adequately informed by international experience, on the other. As an example, the surveys examined in this paper point to the importance of information and suggest that the governments might play a more active role in generating and maintaining more information on SMEs as a public good, including more information on the universe of SMEs and their financial condition and repayment history. This information could be used by the banking system not only to improve risk management but also to asses the size of the market and better target SMEs of particular economic sectors and geographic regions. However, the implications for the credit market of a new balance between public and private information on SMEs would 42
# **Annex IIA: Stamp Duty Rates for States and Union Territories FY81-FY2003**

|**Year**|**Andhr**|**a Prade**|**sh**||**Assam**||**Bihar & J**|**harkhand**||**Goa,**|**Daman &**|**Diu**||**Gujarat**|||**Haryana**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Basic**|**SC**<sup>**1**</sup>|**Total**|**Basic**|**SC**<sup>**2**</sup><br>**Total**|**Basic**|**SC**<sup>**3**</sup>|**Addl. SC**<sup>**4**</sup>|**Total**|**Basic**|**SC**|**Total**|**Basic**|**SC**<sup>**5**</sup>|**Total**|**Basic**|**SC**|**Total**|
|**1980-81**|4.5|5|9.5||0|||||8|0|8|**4**|1|5|12.5|0|12.5|
|**1981-82**|4.5|5|9.5||0|3.5|2.8|0.35|6.65|8|0|8|**4**|1|5|12.5|0|12.5|
|**1982-83**|4.5|5|9.5||0|3.5|2.8|0.35|6.65|8|0|8|**4**|1|5|12.5|0|12.5|
|**1983-84**|4.5|5|9.5||0|3.5|2.8|0.35|6.65|8|0|8|**4**|1|5|12.5|0|12.5|
|**1984-85**|4.5|5|9.5|8.25|8.25|3.5|2.8|0.35|6.65|8|0|8|**4**|1|5|12.5|0|12.5|
|**1985-86**|4.5|5|9.5|8.25|8.25|3.5|2.8|0.35|6.65|8|0|8|**4**|1|5|12.5|0|12.5|
|**1986-87**|5|5|10|8.25|8.25|3.5|2.8|0.35|6.65|8|0|8|**4**|1|5|12.5|0|12.5|
|**1987-88**|5|5|10|8.25|8.25|3.5|2.8|0.35|6.65|8|0|8|**4**|1|5|12.5|0|12.5|
|**1988-89**|5|5|10|8.25|8.25|3.5|2.8|0.35|6.65|8|0|8|**4**|1|5|12.5|0|12.5|
|**1989-90**|5|5|10|8.25|8.25|3.5|2.8|0.35|6.65|8|0|8|**4**|1|5|12.5|0|12.5|
|**1990-91**|5|5|10|8.25|8.25|3.5|2.8|0.35|6.65|8|0|8|**4**|1|5|12.5|0|12.5|
|**1991-92**|5|5|10|8.25|8.25|7|2.8|0.35|10.15|8|0|8|**4**|1|5|12.5|0|12.5|
|**1992-93**|5|5|10|8.25|8.25|7|2.8|0.35|10.15|8|0|8|6|1.5|7.5|12.5|0|12.5|
|**1993-94**|5|5|10|8.25|8.25|7|2.8|0.35|10.15|8|0|8|6|1.5|7.5|12.5|0|12.5|
|**1994-95**|5|5|10|8.25|8.25|7|2.8|0.35|10.15|8|0|8|6|1.5|7.5|12.5|0|12.5|
|**1995-96**|5|5|10|8.25|8.25|7|2.8|0.35|10.15|8|0|8|6|1.5|7.5|12.5|0|12.5|
|**1996-97**|5|5|10|8.25|8.25|7|2.8|0.35|10.15|8|0|8|6|1.5|7.5|12.5|0|12.5|
|**1997-98**|5|5|10|8.25|8.25|7|2.8|0.35|10.15|8|0|8|6|1.5|7.5|12.5|0|12.5|
|**1998-99**|5|5|10|8.25|8.25|7|2.8|0.35|10.15|8|0|8|6|1.5|7.5|12.5|0|12.5|
|**1999-2000**|5|5|10|8.25|8.25|7|2.8|0.35|10.15|8|0|8|6|1.5|7.5|12.5|0|12.5|
|**2000-01**|5|5|10|8.25|8.25|7|2.8|0.35|10.15|8|0|8|8|2|10|12.5|0|12.5|
|**2001-02**|5|5|10|8.25|8.25|7|2.8|0.35|10.15|8|0|8|8|2|10|12.5|0|12.5|
|**2002-03**|5|5|10|8.25|8.25|7|2.8|0.35|10.15|8|0|8|8|2|10|12.5|0|12.5|
|**2003-04**|5|5|10|8.25|8.25|7|2.8|0.35|10.15|8|0|8|8|2|10|12.5|0|12.5|

1.  The surcharge levied on theamount or value of the consideration for the sale or market value of the property, whichever is higher.

3. The rate of surcharge is 85 paise per Re. 1 of stamp duty since 19.1.1972.

4. The rate of additional surcharge is 10% of the amount of stamp duties since 1.12.1977.

5. Additional stamp duty is leviable under Section 3-A of the Bombay Stamp Act (Gujarat), 1958, @ 25% of stamp duty.

66
- 7 -

13. Unfortunately, however, these national-level transport and socioeconomic indicators cannot present actual transport connectivity on the ground. By construction, these indicators barely change over time even if some roads are substantially improved. For instance, road density does not change unless a new road is constructed. In reality, many road projects aim at rehabilitating or maintaining existing roads, not developing new ones. In addition, the indicators aggregated at the national level ignore potential spatial heterogeneity in a country. Transport connectivity often differs significantly across subnational areas.

14. To capture historical changes in transport connectivity more precisely, spatial techniques are used to construct detailed georeferenced data based on stop-by-stop freight shipping data. The CMPP collected by the ADB database takes a time/cost-distance (TCD) approach (UNESCAP).<sup>4</sup> The data is focused on regional long-haul shipments originated from and/or destined for the CAREC member countries over the period of 2010-20. It comprises detailed micro shipping cost and time, which are tracked movements of a truck or cargo from door to door along transport corridors, including borders and other inspection points.

15. The original database contains over 187,000 observations, covering both road and rail shipments. Excluding data with missing information (e.g., origin or destination), the paper uses 93,786 road shipment data, which could be georeferenced. A total of 14,342 shipping carriers transported these cargos among 565 locations.<sup>5</sup> Note that as shown in **Figure 5** , each observation is recorded by a shipping carrier at every major stop point (normally, city or border point). Thus, even on the same road segment, we observe different transport costs and times. For instance, some carriers may have driven faster than others. The figure shows average speeds recorded by two Kazakh shipping carriers in 2013. Origins, destinations and stopped points can be different across carriers. Even if they are different (e.g., between Shymkent and Merke), the speed (or time) data are available at the road segment level. Similarly, the transport costs differ across carriers and the cost data are also disaggregated at the road level.

> 4 See ADB (2014) for detailed data collection methods.

> 5 While 527 locations are identified as origins, 516 locations are georeferenced as destinations.
25

# **Appendix Table 1.  Summary of data on poverty, international remittances and skill composition of migrants**

|**Country**|**Survey Year**|**Poverty**<br>**Headcount**<br>**($1/person/day)**|**Poverty**<br>**Gap**<br>**(%)**|**Gini**<br>**coefficient**|**International**<br>**Remittances**<br>**(million**<br>**dollars)**|**Per Capita**<br>**International**<br>**Remittances**<br>**(dollars)**|**Low-**<br>**skilled**<br>**Migrants**<br>**(%) (Less**<br>**than 8**<br>**years**<br>**schooling)**|**High-**<br>**skilled**<br>**Migrants**<br>**(%) (13**<br>**years or**<br>**more**<br>**schooling)**|**Population**<br>**under 14**<br>**years (%)**|
|---|---|---|---|---|---|---|---|---|---|
|Philippines|2000|15.48|2.98|0.46|125|1.6|12.8|67.1|37.5|
|Poland|2000|0.13|0.06|0.329|639|16.5|30.0|39.5|19.2|
|Romania|2000|2.14|0.59|0.302|2|0.1|34.5|31.3|18.3|
|Russia|2000|6.14|1.19|0.456|363|2.4|26.2|51.1|18.2|
|Slovak Republic|1996|0.49|0.06|0.258|4|0.7|37.9|20.0|20.5|
|South Africa|2000|10.71|1.74|0.577|38|0.8|10.8|62.6|33.5|
|Sri Lanka|1999|7.58|1.49|0.332|1052|55.6|30.7|39.5|26.2|
|Tanzania|2000|56.99|20.64|0.346|35|1.0|22.9|51.3|44.0|
|Thailand|2000|1.93|0.05|0.431|867|14.1|28.9|44.6|25.6|
|Tunisia|2000|0.32|0.07|0.408|796|82.9|73.0|14.9|30.2|
|Turkey|2000|0.87|0.21|0.4|4560|67.6|79.1|8.8|30.7|
|Uganda|1999|26.84|7.74|0.43|232|10.7|23.5|46.2|50.3|
|Ukraine|1999|2.92|0.62|0.289|84|1.7|41.5|33.1|17.9|
|Venezuela|2000|8.87|2.7|0.44|217|8.9|11.1|60.1|33.7|
|Vietnam|1998|3.80|0.48|0.355|3200|40.7|28.5|40.0|33.5|
|Yemen|1998|10.21|2.3|0.334|1202|72.8|33.7|34.5|48.3|

**Notes:** All poverty, inequality, income and remittance data from year of survey; all other data from 2000.

**Sources:** All poverty and inequality data from World Bank, Povcal Net.  All remittance data (except as noted in endnote 3) from International Monetary Fund, <u>Balance of Payments Statistics Yearbook.</u> Data on skill composition of migrants from Docquier and Marfouk (2005), and population data from World Bank, Data Development Program (2006).
# **1. Introduction**

Coastal areas are densely populated and often the most developed stretches of land in many parts of the world.<sup>6</sup> It is estimated that 50 percent of the world’s population lives within 100km of a coastline (World Bank 2009) and expectations are that this figure will grow in the next half century. Climate change is a serious threat to countries with high concentrations of population and economic activity in coastal regions.

The potential impacts of climate change on coastal areas include progressive inundation from sea level rise, heightened storm damage, loss of wetlands, and increased salinity from saltwater intrusion. Most research has focused on the long-run effects of inundation as the sea level rises, along with associated losses from heightened storm surges (for example, Agarwala et al. 2003; Ali 1996, 2003; Ali and Chowdhury 1997; Dasgupta et al. 2009; Dasgupta et al. 2010; Dasgupta et al. 2011; Kabir et al. 2006; Nicholls 2003, 2006; Hanson et al. 2011). The implications of climate change for saltwater intrusion and its impact on livelihoods and adaptation alternatives have not been investigated in great detail (World Bank 2009). However, sporadic measurement of soil and water salinity along coastal areas indicates intrusion of saline waterfront landward in many areas (Mohal et al. 2006). Anecdotal evidence also indicates that increased salinity from saltwater intrusion poses an imminent threat to livelihoods and public health through its impacts on agriculture, aquaculture, infrastructure, coastal ecosystems, and the availability of freshwater for household and commercial use (DFID 2007; Hussain 2008). The problem of saltwater intrusion is expected to become more severe in low-lying coastal areas throughout the world, with increased sea level caused by climate change (Bates et al. 2008; Akhter 2012). Hence, understanding the socioeconomic impacts of salinity ingress and adaptation alternatives is a key requirement for long-term development and are subjects of our ongoing research.

Understanding socioeconomic impacts of salinity ingress in turn calls for the modeling of spatiotemporal diffusion of salinity in a changing climate and an analysis of the physical impacts. As a prerequisite for upcoming analyses of the socioeconomic impacts and adaptation to salinity, this paper presents a study undertaken for Bangladesh to quantify the relationship between climateinduced changes in sea level, temperature, rainfall, altered riverine flows from the Himalayas, and the spread and intensity of the effects of salinization on river water in the coastal zone for

> 6 In the majority of cases, the term “coastal” is defined as a sea-land surface.

2
**Table 4. Survey Methods’ Heterogeneous Effects by Respondent’s Sex or Age**

||(1)<br>D = Fe<br>|(2)<br>male<br>|(3)<br>D = Y<br>|(4)<br>outh<br>|
|---|---|---|---|---|
||Employed|Working|Employed|Working|
|LOA|0.005|-0.001|0.033|0.043*|
||(0.030)|(0.027)|(0.027)|(0.025)|
||[0.863]|[0.971]|[0.215]|[0.062]|
|ESR|-0.022|-0.025|-0.019|-0.029|
||(0.033)|(0.028)|(0.027)|(0.025)|
||[0.493]|[0.353]|[0.481]|[0.244]|
|LOA x D|0.063|0.082**|0.016|-0.007|
||(0.043)|(0.041)|(0.052)|(0.052)|
||[0.175]|[0.059]|[0.752]|[0.879]|
|ESR x D|0.042|0.025|0.069|0.064|
||(0.046)|(0.044)|(0.051)|(0.050)|
||[0.339]|[0.541]|[0.195]|[0.239]|
|LOA + LOA x D|0.068**|0.081**|0.049|0.036|
||(0.033)|(0.033)|(0.045)|(0.045)|
||[0.051]|[0.014]|[0.256]|[0.415]|
|ESR + ESR x D|0.020|-0.000|0.050|0.035|
||(0.034)|(0.034)|(0.045)|(0.045)|
||[0.567]|[0.993]|[0.291]|[0.442]|
|Observations|2,480|2,480|2,480|2,480|
|Outcome(Control Group)Mean|0.571|0.670|0.571|0.670|

_Notes:_ This table presents the treatment heterogeneity of including the LOA module (LOA) or enforcing self-reporting (ESR) by sex and age for conveying employment or work status. We present the ITT estimates from Equation (2) and the total effect of each survey method. The indicator takes the value of 1 if the respondent is a woman (Columns [1] and [2]) and if the respondent is a youth aged 15 to 24 years (Columns iihss [3] and [4]). The vector includes the indicator and the list of control variables selected using a DD double-LASSO procedure for each outcome (Table A3 in the Appendix contains the variables selected). All iihss iihss XX DD estimations include EA (stratification variable) fixed effects. Each column is a separate dependent variable as defined in Section 3. The control mean refers to the mean of the control group for each outcome. Standard - errors clustered at the household level are presented in parentheses, and the randomization inference _p_ values are shown in brackets. *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1 31
An examination of mean age at first marriage, education, and (most tellingly) height, does not suggest systematically worse outcomes for widows relative to non-widows. The mean differences in height ─ 6.8 and 6.0 millimeters in urban and rural Mali respectively ─ actually favor widows although only significantly in rural areas ( _t_ =2.01; urban _t_ =1.36). There is no difference in age at first marriage in urban areas (-0.26, _t_ =-0.90), while widows residing in rural areas were a statistically significant 0.65 years older on average when they first married ( _t_ =2.36). Finally, comparing average years of education reveals that widows as a whole have significantly lower attainments: the difference in means is 1.1 years less ( _t_ =-3.92) in urban and 0.2 years ( _t_ =- 2.37) in rural Mali. Note, however, that widows are on the whole an older group than other women in the sample. Younger cohorts have had greater opportunities to attend school than did older cohorts. Indeed, once I control for age, the education differences and their statistical significance vanish (urban difference is -0.44, _t_ =-1.41; rural difference is 0.08, _t_ =0.95).

Combining these variables in one probit for whether a woman is or has been a widow confirms that only age (including age at first marriage with a significant negative effect in urban areas) and being from the Dogon ethnicity in urban Mali has any explanatory power. The regressions are given in Table 6 for the entire and the urban and rural samples separately. The regressions include height which can reasonably be treated as exogenous for this purpose; there is no significant effect of height on the probability of being a widow.<sup>21</sup> Table 6 also gives regressions including the wealth quintile of the widows‘ households. While this is possibly endogenous to widowhood, it is of interest to test its significance. If widowed women come from poorer backgrounds and married poorer men then, by the same logic, they are also likely to remarry into relatively poorer households (whether they choose the levirate or not) or to join a household ─ typically that of their parents, a brother or a son ─ that is also poor. Hence, if the US type of selection into widowhood is true for Mali, one would expect widowhood to be highly correlated with living in a low wealth household. But, as can be verified in Table 6, I find no such evidence. There is no significant partial correlation between widowhood and household wealth, controlling for age, height and ethnicity. The results without controlling for wealth are

> 21  The controls are age and age squared, age at first marriage, dummies for wealth quintile, education, whether born in an urban area, height, rural or urban residence and ethnic group. The wealth quintiles are calculated by the DHS based on an index of assets owned, housing and basic infrastructure attributes generated using principal components analysis.  It is a household level index, not a per capita one.

19
following the empirical literature (e.g. Asiedu, 2002). Note that for the three latter variables, a higher value corresponds to a lower risk. High levels of inflation, exchange rate instability generating currency mismatches, and macroeconomic and financial uncertainty indicating the likelihood of eventual crises are all expected to have negative effects on foreign direct investment attraction.

Aside from endowments, location-bound, and macroeconomic variables, institutions are widely considered to matter for foreign direct investment attraction. For example, as argued by Rodrik, Subramanian, and Trebbi (2004:21), the presence of clear property rights for investors is a key, if not the key, element in the institutional environment that shapes economic performance. A country in which investors believe their property rights are safe would thus be more likely to attract FDI as shown for the first time by Alfaro, Kalemli-Ozcan and Volosovych (2008) and then by others.

Therefore, as discussed in more detail in Sánchez-Martín et al. (2014), the proposed empirical approach pays special attention to institutional variables and introduces a series of institutional indexes elaborated by the PRS Risk Group (International Country Risk Guide), such as _control of corruption, rule of law, government stability, bureaucracy quality,_ and _investment profile_ (see table with definitions in the Annex) _._ In principle, it would be expected that the higher a country rates in these institutional dimensions, the larger FDI inflows that country would be receiving. Higher control of corruption (less likelihood of patronage, nepotism...) is expected to result in larger FDI inflows, according to the so called “grabbing hand” hypothesis by Egger and Winner, 2006. A _law and order_ index, composed of an assessment of the strength and impartiality of the legal system and an assessment of the popular observance of law, is also included in the analysis, and expected to be a key factor for FDI attraction. The _government stability_ variable, a valuation of the government’s ability to carry out its electoral program and its ability to stay in office, could be expected to positively contribute to foreign direct investment attraction, although empirical evidence is mixed (Asiedu, 2002). The _quality of bureaucracy_ is believed to foster foreign direct investment attraction in the sense that it entails lower administrative hurdles and enhanced policy implementation. Finally, _investment profile_ , measuring the likelihood of expropriation, obstacles to profit repatriation, payment delays and risks posed to contract viability is also expected to present a positive sign, given that the higher the rating the lesser the direct obstacles to investment are.

Given the nature of our sample – mostly composed of Eastern European countries during the post-communist era- it is important to add control variables for the important economic transformation these economies have been through. Thus, in order to take into account the recent conversion of Eastern European economies to the capitalist economic system, the cross country 15
income or expenditure on transport are necessarily better off than people that spend more. As an example, it may be that due to the high price of public transport very poor people either walk or do not make many trips. Thus, their observed transport expenditure may be low but this is the result of a suppression of trips rather than a high level of income.

Figure 1 presents an example which may be quite typical of cities in poor developing countries. Low income households may actually spend less than middle income households on transport. This is due the prevalence of walking as one of the main modes of travel.

**Figure 1: Expenditure on public transport by income level**

<!-- Start of picture text --> Affordability<br>benchmark<br>Household Income or expenditure<br>Percentage of household income or expenditure spent on public transport<br><!-- End of picture text --> A naïve interpretation of the affordability data from Figure 1 would indicate that only middle-income households require a subsidy given that they spend more on transport than the predefined affordability benchmark. The reason why the affordability benchmark fails in this case is due to the fact that financial (observed) transport expenditure is not the same as the generalized transport cost. If a shadow cost is used to value walking trips — to account for the relative discomfort, time and energy required compared to other modes of travel— then the expenditure on transport by the poor would increase substantially. In addition, it may be that poor people demand fewer trips due to the high cost of travel.

In order to overcome the above problem Carruthers, Dick and Saurkar (2005) use a fixed basket of trips to estimate an affordability index. They define affordability as “the ability to make necessary journeys to work, school, health and other social services, and make visits to other family members or urgent other journeys without having to curtail other essential activities”. Operationally, they use the percentage of monthly per capita income level. Using personal expenditure and income data to calculate the affordability index does not solve this problem since family resources are shared to some extent within the household unit.

11