rafmacalaba/datause-encoder-data huggingface dataset:
highest risk of falling into poverty, say, 15 percent, that is deemed socially acceptable; another is to put forward a social protection target that aims to reduce vulnerability to below a certain level (which can be similar to the recent goal of reducing the global headcount poverty to 3 percent or less by 2030 proposed by the World Bank). We will employ a vulnerability index of 20 percent and the associated vulnerability line for our welfare analysis in the next section, but we will also provide, for comparison purposes, some estimates that are based on twice the national poverty line (i.e., 893.4 rupees per month in 2004 price for all rural India) as with the existing practice in the country. Table 4 indicates that doubling the national poverty line would roughly translate into a vulnerability index of between 21 and 22 percent for 2004-2009, and between 19 and 20 percent for 2009-2011.

# **V. Welfare Dynamics Analysis**

We have discussed the changes in poverty over time in the previous section, thus will focus on discussing the other dynamics with vulnerability in this section. We start first with showing the welfare transitions for all the population before delving further into population groups.

# **V.1. All Population**

The welfare transition matrixes for the three consumption groups for the two periods 2004-09 and 2009-11 are respectively shown in Panel A and Panel B in Table 5, where the vulnerability index is fixed at 20 percent for both periods.<sup>23</sup> Together with the decrease in poverty, there is an expansion of the vulnerable and the middle class (categories) in the period 2004-09. This trend

> associated with this figure. For India during the period 2004-09, the associated vulnerability index and vulnerability line would be respectively 26 percent and 675 rupees per month (Table 4).

> 23 As noted earlier, we restrict the data to households whose head’s age is between 25 and 55 in the first survey round and adjust accordingly for the second survey round (e.g., age ranges 25-55 for 2004/05 and 30- 60 for 2009/10 in the period 2004-09) to keep household units stable. This results in some slight differences with poverty rates based on this data compared to the full data. We are using the first and second survey rounds respectively as the base and target surveys for constructing the synthetic panels. For these reasons, the estimated poverty rate for 2011/12 slightly change from 23.7 percent (Tables 5 and 6) to 25 percent (Table 7) below. This slight difference appears not very large in practice and is consistent with the imputation-based estimates of poverty in section III.

21
|Country|2011 GNI per capita<br>(Atlas methodology <br>current US$)|Income<br> Classification|Public<br>debt/GDP|Date of DeMPA<br>Assessment|Country|2011 GNI per cap<br>(Atlas methodol<br>current US$)|ita<br>Income<br>ogy Classification|Public<br>debt/GDP|Date of DeMPA<br>Assessment|
|---|---|---|---|---|---|---|---|---|---|
||_Africa (8)_|||||_Middle East and_|_North Africa (1)_|||
|Cape Verde|3540|lower middle|71|Feb-09|_Djibouti_|_1280_|lower middle|55|Apr-10|
|Comoros|770|low|43|Apr-11||||||
|Gabon|7980|upper middle|21|May-12|_Latin Ameri_|_ca and the Caribbe_|_an (4)_|||
|Gambia|610|low|68|Mar-06 and Jan-10|Antigua and Barbuda|12060|upper middle|93|Jun-09|
|Guinea-Bissau|600|low|44|Sep-11|Grenada|7220|upper middle|104|Jun-09|
|Namibia|4700|upper middle|25|Mar-12|_Guyana_|_3270_|lower middle|65|Apr-06|
|Sao Tome and Principe|1360|lower middle|48|Feb-08 and May-11|St Kitts and Nevis|12480|upper middle|154|Apr-09|
|Swaziland|3300|lower middle|14|June-08 and Mar-11||||||
|||||||_South Asia (2)_||||
||_East Asia (2)_||||Bhutan|2070|lower middle|81|Dec-11|
|Samoa|3190|lower middle|53|Jan-10|Maldives|6530|upper middle|88|Oct-09|
|Solomon Islands|1110|lower middle|21|Feb-09||||||

Source: World Bank and IMF Note: countries in italics are eligible for the HIPC and MDRI initiatives The evaluation of debt management performance in small states presented in this paper is based on DeMPA for the 17 small states listed in Table 1 above and it may not be an accurate reflection of the strengths and weaknesses of debt management performance in other small states. However, the convergence of DeMPA findings in the 17 small states assessed is striking, and indicates broader lessons may apply. The average income, and debt related characteristics, of small states in the sample group is representative of the average for all small states. The average GNI per capita of small states in the sample group is only slightly lower, US$4,329 than that of all small states (US$4,759) and the ratio of domestic public debt to GDP is within the same range (Table 2). As a share of total outstanding external debt of small states, the share owed by those in the sample group has consistently been between 40 and 45 percent throughout the past decade. However, ratio of external public debt to GDP is higher 62 percent (compared to 52 percent) but this is explained by the fact that the sample group includes all five of the most heavily indebted small states, with public debt to GDP levels above 80 percent. A higher share of small states in the sample group had recourse to debt restructuring and this could be taken as an indicator of weaker debt management capacity. However, this is counterbalanced by the fact that almost all of them have ongoing reform programs with the IMF. These programs include a commitment to fiscal consolidation and structural reforms that may be expected to result in improvements in debt management and better coordination with fiscal and monetary policies.

**Table 2: Comparison of Key Indicators**

_Percent_ 11
# **III. Methodology and case**

Case studies were conducted in seven sites located in the states of Bihar, Jharkhand, Odisha, and West Bengal. These states were chosen to better understand the type of urbanization transition in poorer states with a low rate of urbanization and in West Bengal, which ranks second in 2011 for the increase in the number of CTs. As one can see in Table 1, census towns are an extensive contributor to the extent of urbanization in all the states, except Bihar. In the three other states, the contribution of new CTs to the urban population is significantly higher than the Indian average. In the case of West Bengal, the increase is as high as 66% (second only to Kerala). Even in the case of Bihar where the contribution of new CTs to the urban population growth is lower than in the other states, 52 new CTs have been added in 2011.

**Table 1. Census Town (CT) and Extent of Urbanization 2011**

||Census level of<br>Urbanization<br>(% of state population)|Population of New CTs as<br>share of growth in state’s<br>urban population (%)|Number of<br>New CTs in<br>2011|Number of Total<br>CTs in 2011|
|---|---|---|---|---|
|Bihar|11.3|13.9|52|60|
|Jharkhand|24.1|44.7|107|188|
|Odisha|16.7|38.3|86|116|
|West Bengal|31.9|66.0|526|780|
|All India|31.2|29.5|2553|3892|

Source: Authors’ analysis At least one Census Town was identified in each state (in Jharkhand, a pair of adjoining Census Towns were studied), and a methodology was applied to identify candidate settlements which would be as representative as possible. In Bihar, three settlements including a Census Town, a large village, and a Statutory Town - all located in the same district – were selected as case studies. The list of settlements selected is presented in Table 2.

**Table 2. Case studies**

|State|Settlement|Status|Population (census 2011)|
|---|---|---|---|
|Bihar|Satghara|Census Town|8,060|
|Bihar|Jhanjharpur|Statutory Town|30,590|
|Bihar|Simri|Village|10,846|
|Jharkhand|Cherra|Census Town|5,279|
|Jharkhand|Bishnugar|Census Town|4,847|
|Odisha|Erein|Census Town|7,890|
|West Bengal|Gopalpur|Census Town|7,016|

5
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE than 100 kilowatt-hours per capita per year is less than 1 percent of levels in OECD countries, and barely enough to power one light bulb per person for three hours per day. Electricity access remains low at just over 10 percent of the population; even in urban areas only 40 percent of the population has access to power, while in rural areas that number lies below 2 percent. As of the mid 2000s, Tanzania was electrifying less than one percent of the population annually. Unless this pace is accelerated universal electrification could take another 100 years to achieve.

Looking ahead, Tanzania has the potential to play a significant role in regional power trade within the framework of the East African Power Pool, as well as the Southern Africa Power Pool. A simulation exercise exploring Tanzania’s potential contribution to a fully integrated East African Power Pool finds that if the region pursued power trade to its fullest economic potential, Tanzania could emerge as a power exporter with exports rising to 2.4 TWh over a decade, equivalent to over 20 percent of future domestic consumption (Rosnes and Vennemo, 2009). Tanzania’s long-term marginal cost of power generation – was estimated at $0.065 per kilowatt hour. Trade expansion would require Tanzania to build an additional 1,200 megawatts of power generation capacity over the next decade beyond that required to meet domestic demand, as well as 266 megawatts of new cross-border transmission. This would require investment of $150 million per year, over and above what is needed to meet domestic power needs. Nevertheless, the relative positions of East African countries with respect to energy resources is evolving rapidly with the discovery of new geothermal and wind resources, which may affect the conclusions of the 2009 study.

**Figure 4. The cost of power outages in Tanzania is higher than in other East African countries, 2006**

<!-- Start of picture text --> Tanzania<br>Uganda<br>Kenya<br>Senegal<br>Madagascar<br>Cape Verde<br>Niger<br>Benin<br>Cameroon<br>Burkina Faso<br>0.0 1.0 2.0 3.0 4.0 5.0<br>% of GDP<br><!-- End of picture text --> _Source:_ Eberhard and others 2009.

9
|**Table 8: Impact of Business Training**|**on Business Practice**|**s**|**Number**|||
|---|---|---|---|---|---|
|**Study**|**Gender**|**Units**|**of Practices**|**Point estimate**|<br>**95% CI**|
|Berge et al. (2012)|Male|p.p.|4 (a)|0.03-0.08|n.a.|
||Female|p.p.|4 (a)|-0.02-0.00|n.a.|
|Bruhn and Zia (2012)|Mixed|s.d.|3|**0.272**|**(0.03, +0.51)**|
||Male|s.d.|3|**0.290**|**(0.01, 0.57)**|
||Female|s.d.|3|0.214|n.r.|
|Calderon et al. (2012)|Female|p.p.|1|0.062|(-0.02, +0.14)|
|De Mel et al. (2012)||||||
|Current Enterprises|Female|num|29|**2.03**|**(1.27, 3.30)**|
|Potential Enterprises|Female|num|29|0.87|(-0.23, +1.97)|
|Drexler et al. (2012)||||||
|"Rule-of-thumb"|Mostly Female|s.d.|12|**0.14**|**(0.06, 0.22)**|
|"Standard"|Mostly Female|s.d.|12|0.07|(-0.03, 0.17)|
|Giné and Mansuri (2011)|Mixed|s.d.|3|**0.131**|**(0.01, 0.25)**|
||Male|s.d.|3|0.114|(-0.05, 0.28)|
||Female|s.d.|3|0.140|n.r.|
|Karlan and Valdivia (2011)|Mostly Female|s.d.|14|**0.03**|**(0.00, 0.06)**|
|Mano et al. (2012)|Male|p.p.|3|**0.24-0.42**|n.a.|
|Valdivia (2012)||||||
|General training|Female|s.d.|11|0.01|(-0.02, +0.04)|
|Training+ technical assistance<br>_Notes:_<br>95% CI denotes 95 percent confidenc<br>Units for measuring practices are eit<br>points (p.p.), or number of distinct p<br>Number of practices is the total num<br>When no aggregate measure is repo<br>n.r. denotes not reported. n.a. deno<br>(a) we include here their index of th<br>measure is provided.|Female<br>e interval. Impacts s<br>her standard deviati<br>ractices improved (n<br>ber of practices mea<br>rted, the range of po<br>tes not applicable si<br>ree marketing practi|s.d.<br>ignifican<br>ons of a n<br>um).<br>sured.<br>int estim<br>nce range<br>ces, plus|11<br>t at the 10% l<br>ormalized ag<br>ates for indiv<br>of estimates<br>their result o|**0.05**<br>evel or more re<br>gregate (s.d.),<br>idual practices<br>given.<br>n record-keepi|**(0.02, 0.08)**<br>ported in**bold.**<br>percentage<br>is given.<br>ng. No aggregate|

34
- 41 -

(IFPRI) also emphasize the importance of looking at the household complex and its many tasks, from production to consumption. (Rockefeller Foundation/ISNAR 1985).

<!-- Start of picture text --> Box 9: RESEARCH AGENDA FOR UMEM FARNERS<br>CONSTRAINTS RESEARCH SOLUTIONS<br>1. LAND<br>Limited Land . Multiple and relay cropping.<br>. Intensification using fertilizer.<br>. Higher value enterprises requiring littLe land, such as poultry,<br>or rabbits.<br>. Value acdded through processing.<br>. Terracing and mutching.<br>Problems with Fertility Alley cropping.<br>.Mixed farming, mixed crop/tivestock systems using small ruminants.<br>F fertilizers.<br>. Soit conservation techniques.<br>. Use of household refuse, compost-making.<br>2. CREDIT . Low-input technologies requiring management rather than<br>purchased inputs.<br>. Inputs in smaller packages.<br>. Credit groups--traditional/new.<br>3. LABOR<br>AvaiLability/Seasonal Variations . Labor-reducing technoLogies such as herbicides and multiple<br>cropping.<br>. Appropriate mix of farm enterprises.<br>Physical Strengths/Weaknesses Appropriate technologies for such strenuous tasks as<br>water collection and food processing.<br>. Tool and machine design.<br>. Application rates in measures which can be transported and used<br>easily.<br>4. TECHNOLOGIES . Animal traction.<br>. Tocts such as hoes, smatt tilters, carts.<br>. Smatl affordable irrigation devices.<br>M achines for harvesting/processing.<br>5. EDUCATIONAL LEVEL . Recommendations in visual and oral form.<br>6. SOCIOCULTURAL . Enterprises acceptable for women.<br>. Appropriate extension detivery systems.<br>(Spurling, 1989)<br><!-- End of picture text -->
female, age 15-49. We thus have information on the date of birth, the sex and - in case of death - also the date of death of all siblings of the adult female interviewed in the 2000 DHS. One advantage of the mortality module is that it greatly increases the size of the population from which mortality estimates are calculated. Using this module, we increase the number of cases in our analysis from roughly 10,000 adult females to close to 45,000 siblings of the survey respondents. In addition, compared to many studies about mortality, it is not drawn from a sample of convenience (e.g. refugees), since the DHS are administered to a randomly selected representative sample of the population.

There are however several methodological issues implied by using a sample of survivors. Even with a nationally representative sample, estimates based on a sample of survivors are potentially subject to several shortcomings (see for example, Heuveline, 1998, 2001 (a), 2001 (b) about estimates of the number of deaths during the Khmer Rouge period in Cambodia). First, the method relies on recall of past events and is therefore possibly prone to misreporting. Several tests of data quality are performed in the DHS report and there does not seem to be systematic reporting biases; the sex ratios at birth are in the internationally acceptable range suggesting that there is no serious under- or over-reporting of brothers or sisters. Similarly, the median year of birth is the same for respondents as for siblings, indicating that there is no underreporting of older siblings. When more than one respondent in the same household reported on the same sibship, only one respondent was randomly retained.

Given the way the adult mortality schedule is constructed, we miss entirely families in which only males were born and we are likely to under-represent families where a majority of males where born. But this is only likely to have an impact if we 7
44 education, 1st wealth quintile (poorest), Nairobi province. Full immunization variable measures fraction of children with all vaccinations at district level. Vaccination records are not available for deceased children. For definition of "all vaccinations" see the Annex. Significance levels are marked as follows: * 10%, ** 5%, *** 1%.

**Table 7: Oaxaca-Blinder decomposition of the difference in postneonatal** **<u>and infant mortality between 2003 and 2008 in Kenya</u>**

||<br>Neonatal|<br>Postneonatal|Infant|
|---|---|---|---|
|2003 survey|34.32***|46.62***|80.94***|
|2008 survey|38.34***|21.85***|60.19***|
|Difference between 2003 and 2008|-4.02|24.77***|20.75*|
|Explained part of the difference|2.03|12.82*|14.86*|
|Unexplainedpart of the difference|-6.05|11.94|5.89|
|Low malaria risk zone (LMRZ)|-0.07|-0.17|-0.24|
|Intermediate malaria risk zone (IMRZ)|0.11|-0.70|-0.58|
|High malaria risk zone (HMRZ)|-0.26|-1.85|-2.11|
|Interaction LMRZ and ITN ownership|-1.16|-1.73|-2.89|
|Interaction IMRZ and ITN ownership|1.83|-0.96|0.87|
|Interaction HMRZ and ITN ownership|2.34|9.73**|12.07**|
|Full immunization (district level)|-4.23|7.68|3.44|
|Born in a facility|-0.35|0.06|-0.28|
|Antenatal tetanus protection|-0.01|-0.12|-0.13|
|Antenatal antimalarial drugs|2.13|0.05|2.18|
|Antenatal iron supplement|0.62|0.29|0.91|
|Surface water|-0.26|0.76|0.50|
|Unimproved water source (not surface)|0.28|-0.74|-0.46|
|Improved water source|0.06|-0.01|0.05|
|No sanitation|0.39|0.50|0.90|
|Unimproved sanitation|0.05|-0.02|0.03|
|Improved sanitation|0.77|0.47|1.24*|
|Mother has no education|-0.62|-0.09|-0.71|
|Mother has primary education|0.23|-0.23|-0.01|
|Mother has secondary education|0.40|0.08|0.48|
|Mother has tertiary education|-0.33|0.06|-0.27|
|1st wealth quintile (poorest)|0.09|-0.17|-0.08|
|2nd wealth quintile|0.01|0.01|0.01|
|3rd wealth quintile|-0.01|-0.01|-0.02|
|4th wealth quintile|0.00|0.00|0.00|
|5th wealthquintile(richest)|0.04|-0.08|-0.04|
|Number of observations overall|4668|4668|4668|
|Number of observations in 2003|2295|2295|2295|
|: Authors ̳ calculations using Demographic and<br> <br>Number of observations in 2008|Health Surveys m<br> <br>2373|icrodata.<br> <br>2373|<br>2373|

Source: Authors ̳ calculations using Demographic and Health Surveys microdata. Notes: Based on regressions in column 6 of Tables 4 and 6. Shown is only decomposition of the explained part of the difference. Contributions of explanatory variables sum up to the explained part of the difference. Significance
Column 1 of Table 8 reports results pertaining to use of internal funds, families and moneylenders as a source of working capital financing. Estimates show owners with more education and a job in the formal sector are less likely to rely on these sources. With more developed financial and legal systems, reliance on these sources also decreases. Next, column 2 shows results for bank financing. Likelihood of a firm using bank financing decreases if it is micro-sized, while owner’s job in the formal sector and country’s level of financial and legal sector are positively associated with using banks for financing. Correlation between trade credit financing and firm level characteristics are not very strong; however, it is positively associated with owner’s level of education and financial and legal sector development of a country. MFI financing is also positively associated with level of owner’s education and financial and legal sector development of a country. MFI financing is negatively associated with firm size.

# **6. Conclusion**

The objective of the paper was first, to document salient features of informal firms, their use of finance and their financing patterns. Second, to compare and quantify the differences that exist in the use of finance by firms in the formal and informal sector, and lastly to identify the most significant characteristics of informal firms that are associated with higher financial inclusion. The paper utilizes data on informal firms collected by World Bank’s Enterprise Surveys.

The analysis shows that firms in the informal sector are on average micro-sized and relatively younger, less than 11 years of age. Most of the owners of informal firms have some form of educational or vocational training and a very small proportion have jobs in an established formal business. Use of loans and bank accounts for business by informal firms is very low and a significant majority of firms finance their day-to-day operations and investments through sources other than financial institutions (internal funds, moneylenders, family and friends). Most of the firms would like to register but tend not to opt for it due to tax reasons and state that relatively easier access to finance would be the most important benefit they could obtain from registering.

A comparative analysis between formal and informal small-sized firms shows, not surprisingly, a higher use of bank accounts and loans by registered firms. Firms in the formal sector are 54 percent more likely to have a bank account and 32 percent more likely to have a loan. On average, registered firms rely less on internal funds, families and friends and on moneylenders for financing their working 26
**Table 2: Household participation in non-farm employment in rural Malawi, 2010/2013**

||Partici<br>rates|Ma<br>pation<br>(%)|lawi<br>Shar<br>House<br>Incom<br>Sourc|e of<br>holds<br>e from<br>e(%)|Partici<br>rates|Non<br>pation<br>(%)|Poor<br>Shar<br>House<br>Incom<br>Sourc|e of<br>holds<br>e from<br>e(%)|Partici<br>rates|Po<br>pation<br>(%)|or<br>Sha<br>Hous<br>Incom<br>Sourc|re of<br>eholds<br>e from<br>e(%)|P-valu<br>Partic<br>rate|e t-test<br>ipation<br>s (%)|poor vs. n<br>Sha<br>Hous<br>Incom<br>Sour|on poor<br>re of<br>eholds<br>e from<br>ce(%)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||2010|2013|2010|2013|2010|2013|2010|2013|2010|2013|2010|2013|2010|2013|2010|2013|
|Non-<br>Agricultural<br>wage|17.1|15.1|8|6.5|21.6|19|10.6|8.5|9.7|7.4|3.9|2.3|0.000|0.000|<br>0.000|0.000|
|Self<br>Employment|18.2|26.8|7.2|11.1|20.3|30.5|9|12.9|14.9|19.4|4.4|7.4|0.002|0.000|<br>0.076|0.000|

Source: Malawi IHS3 panel sample (2010) and IHPS (2013) 27
reason, a bilateral comparison between China and the United States is subjected to a lot of unusual methodological problems, as China has a nmuch lower income level and very different in3titutions. This study should therefo be consdered a very prdimiay comparison. It does not attempt to give a point esimate with certainty, but only possible orders of magnitude vithin which China's dollar per capita GDP night be. So it is vety important to analyze the sources of erors and figure out a possible margin for the esimates.

65. ICP study shows that deleting Come sub-aggrgates will afect the final results. To analyze the effects of errors in the comparisons of gross capital fmation, the results of the comparisos after deleting one to five sub-aggregates from the original sen sub-aggregates were calculated and compared It wuas fund that eors ie as the zmer of the deleted increases 66. Because the national aveae prices were not aviable in some categozies for China, an average of prices coilected in ten cities was regarded as the national aveage prce, It was not possible to mcorpomte pnce infation from mral areas in the calculation of the national avge pnce, due to the lmitations oftime and resources. This should lead to distortion into the final results.

67. In tis study, an effort has been made to estimate some prices where markets still do not dear or even do not exist in the benchmark year in China, as in the cases of ret, health services, education and govenent. On the one hand it seems that the adjustments made in this study are not enough and an upwards bias may stfl exist in esmating real income. On the other hand these 'shadow prices" were not matched by an increase in the estimate of reevant penditnu, and this might mean a downwards bias in estimating real income. 68. Another source of errors is the use of prices of some imported or wjuy goods in the survey. For some categories, there are no comparable domestic goods, so imported goods wer taken as 29