rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Descriptive Statistics: Rural model

||**2004/05**|**2009/10**|**2011/12**|**2014/15**|
|---|---|---|---|---|
|Log HH per capita expenditure<br>Mean|4.18|4.25|4.40|.|
|HH size|||||
|1 or 2|0.05|0.06|0.06|0.06|
|3|0.08|0.09|0.09|0.09|
|4|0.16|0.19|0.19|0.20|
|5|0.19|0.20|0.21|0.21|
|District HH size|||||
|Share 1 or 2|0.05|0.06|0.06|0.06|
|Share 3|0.08|0.09|0.10|0.10|
|Share 4|0.17|0.19|0.20|0.21|
|Share 5|0.20|0.20|0.21|0.21|
|HH age structure|||||
|Share 0-14|0.38|0.35|0.34|0.33|
|Share 15-24|0.16|0.16|0.16|0.16|
|Share 25-34|0.15|0.15|0.15|0.15|
|Share 35-49|0.17|0.19|0.19|0.20|
|Share 50-64|0.10|0.11|0.11|0.11|
|District avg HH age structure|||||
|Share 15-24|0.16|0.17|0.17|0.17|
|Share 25-34|0.15|0.15|0.15|0.16|
|Share 35-49|0.18|0.19|0.19|0.20|
|Share 50-64|0.10|0.11|0.11|0.11|
|Religion and social group|||||
|Hindu|0.84|0.84|0.83|0.83|
|Share Hindu in district|0.82|0.82|0.82|0.81|
|Share sched caste in district|0.71|0.73|0.74|0.76|
|Household type|||||
|Self-employed|0.56|0.52|0.55|0.58|
|Share self-employed in district|0.54|0.51|0.53|0.55|
|Share casual labor in district|0.03|0.03|0.03|0.04|
|Share reg wage worker in distric|0.07|0.07|0.07|0.08|
|Principal industry|||||
|Agriculture|0.65|0.62|0.58|0.62|
|Industry|0.15|0.18|0.20|0.19|
|Share industry in district|0.17|0.20|0.22|0.21|
|HH expenditure|||||
|Avg misc services in district|1587|1536|1828|2602|
|Misc services<br>|1464<br>|1361<br>|1619<br>|2256<br>|
|Avg rec services in district|168|147|187|326|
|Recreational services|141|116|152|276|
|Avg transport expenses in distri|3020|3320|4414|3309|
|Transport expenses|2546|2816|3779|3039|
|Avg transport expenses no fuel i|1859.51|1765.98|2221.08|3110.80|
|Transport expenses no fuel|1729.88|1632.93|2063.66|2872.47|
|Avg transport expenses no fuel a|530.47|584.66|742.01|958.93|
|Transport expenses no fuel and b|409.70|478.95|611.28|811.38|
|District rainfall shock|||||
|July-September|-0.22|-0.10|0.42|0.02|
|July-September (squared)|0.27|0.21|0.38|0.22|
|October-December|-0.30|0.26|-0.62|-0.07|
|October-December (squared)|0.23|0.43|0.51|0.26|
|January-March|0.30|-0.30|-0.22|0.63|
|January-March (squared)|0.43|0.22|0.29|1.15|
|April-June|-0.21|-0.14|-0.15|0.34|
|April-June (squared)|0.35|0.43|0.17|0.34|

24
21 available from: http://ec.europa.eu/internal_market/bank/docs/guarantee/jrc- <u>annex1_en.pdf</u> and http://ec.europa.eu/internal_market/bank/docs/guarantee/jrc- <u>annex2_en.pdf.</u> European Federation of Deposit Insurance, 2006, _Deposit Guarantee Systems: EFDI’s First Report_ , <u>http://efdi.eu/fileadmin/user/publications/EFDI%20publications/Deposit%20Guarantee% 20Systems%20EFDIs%20First%20Report%20%28Manuela%20De%20Cesare%202006 %29.pdf</u> Financial Stability Board, 2012, _Thematic Review on Deposit Insurance Systems_ , <u>http://www.financialstabilityboard.org/publications/r_120208.pdf</u> Financial Stability Board, 2010, _Update on Unwinding Temporary Deposit Insurance Arrangements_ , http://www.financialstabilityboard.org/publications/r_1006.pdf

- Garcia, G., 2000, _Deposit Insurance: Actual and Good Practices_ , IMF Occasional Paper No. 197 (Washington, DC: International Monetary Fund).

- International Association of Deposit Insurance, 2008, _2008 Annual Survey Results of Deposit Insurance_ , http://www.iadi.org/Research.aspx?id=58

- International Association of Deposit Insurance, 2010, _2010 Annual Survey Results of Deposit Insurance,_ <u>http://www.iadi.org/Research.aspx?id=58</u>

- International Association of Deposit Insurance, 2011, _2011 Annual Survey Results of Deposit Insurance_ , http://www.iadi.org/Research.aspx?id=58

- Kane, E.J., 1989, _The S&L Insurance Mess: How Did it Happen?_ (Washington, DC: The Urban Institute Press).

Laeven, L., 2013, _European Union: Publication of Financial Sector Assessment Program Documentation—Technical Note on Deposit Insurance_ , IMF Country Report No. 13/66, (Washington, DC: International Monetary Fund).

<u>http://www.imf.org/external/pubs/ft/scr/2013/cr1366.pdf</u> Laeven, L., and F. Valencia, 2012, “Systemic Banking Crises Database: An Update,” IMF Working Paper no. 12/163 (Washington, DC: International Monetary Fund). <u>http://www.imf.org/external/pubs/ft/wp/2012/wp12163.pdf</u>
Policy Research Working Paper 10781

# **Abstract**

Economic zone programs are generally pursued to improve firms’ performance within discrete areas by removing the constraints firms face. Whether or not they succeed in doing so is an empirical question. This paper capitalizes on a unique survey of firms within and outside zones in South Asia to assess the effects of zone programs on firms’ performance in exports, investment, employment, and productivity. Adopting a propensity score matching approach and district-level fixed effects, the paper explores four questions: whether zones support firm performance; whether the type of zone makes a difference, which kinds of public support services matter more, and whether firms inside zones grow faster. The results show that (i) being inside a zone positively affects foreign direct investment and employment, (ii) the effects across zone types are mixed, (iii) infrastructure and trade facilitation play a greater role in firm performance than fiscal incentives and governance facilities, and (iv) firms inside zones grow faster.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at rgalal@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
range of individual characteristics (Blumenstock et al., 2015), and could conceivably be used to predict other outcomes of interest—such as lifetime customer value, or the social impact of a loan.

It has been widely acknowledged that mobile phones can enable low cost money transfers and savings in developing countries (Suri, Jack, & Stoker, 2012). Our results suggest that nuances captured in the use of mobile phones themselves can alleviate information asymmetries, and thus can form the basis of new forms of low cost lending. These tools together are enabling a new ecosystem of digital financial services.

# **7. REFERENCES**

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## _and Proceedings_ .

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## _Working Paper_ .

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FSD. (2016). FinAccess Household Survey.

22
Khalil, G. 1992. Cyclones and Storm Surges in Bangladesh: Some Mitigative Measures. Natural Hazards. Vol. 6, pp 11‐24 Knutson, T. R., and R. E. Tuleya. 2004. Impact of CO2‐induced Warming on Simulated Hurricane Intensity and Precipitation Sensitivity to the Choice of Climate Model and Convective Parameterization. _Journal of Climate_ 17: 3477‐95.

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Rahmstorf, Stefan. 2007. A semi‐empirical approach to projecting future sea‐level rise. ‐ Science, 315, 368‐370. http://www.pik <u>potsdam.de/~stefan/Publications/Nature/rahmstorf_science_2007.pdf</u> Shultz, J. M., J. Russell, and Z. Espinel, 2005. Epidemiology of Tropical Cyclones: The Dynamics of Disaster, Disease, and Development. _Epidemiologic Reviews_ 27: 21‐35.

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**Agriculture.** Despite growing urbanization in developing countries, poverty continues to be a largely rural phenomenon. Three‐quarters of the poor in developing countries are located in rural areas.<sup>14</sup> Among the 3 billion rural inhabitants in developing countries, 86 percent rely on agriculture for their livelihoods. Consequently, this sector has a critical role to play in poverty alleviation efforts. The 2008 World Development Report found that GDP growth in agriculture is at least twice as effective in reducing poverty as non‐agricultural growth.

Agricultural productivity increases are not only a source of economic growth but also catalyze the development of related industries—accounting, in some cases, for about a third of the GDP—and the broader nonfarm economy. The linkages between agriculture and industry illustrate the importance of agricultural growth as a labor‐intensive process. Productivity growth in agriculture keeps food prices from rising, resulting in low wages that contribute to the competitiveness of non‐agricultural sectors. Agricultural growth is also known to generate multiplier effects on the rest of the economy. It is not surprising that the most successful development stories begin with agriculture.

**Figure 5: Rising labor income is the main driver of poverty reduction**

<!-- Start of picture text --> 140 Decomposition of Changes in Moderate Poverty, by Income Level, in Selected Developing<br>Countries, 2000s<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>US$1.25 a day US$4 a day US$5 a<br>day<br>Share of working-age family members Employment+earnings Nonlabor income Consumption-to-income ratio<br>Source: Figure 1.1 (page 3) in Inchauste and J. Saavedra‐Chanduvi, (2014); using Data from SEDLAC,<br>various years; FAO n.d.; and national household surveys<br>Contribution to change in poverty, percent<br>Bangladesh, 2000–10 Ghana, 1998–2005 Nepal, 1996–2003 Cambodia, 2007–10 Mongolia, 2007–11 Philippines, 2006–09 Sri Lanka, 2002–09 Vietnam, 2004–10 Argentina, 2000–10 Brazil, 2001–09 Chile, 2000–09 Colombia, 2002–10 Costa Rica, 2000–08 Ecuador, 2003–10 Honduras, 1999–2009 Panama, 2001–09 Paraguay, 1999–2010 Peru, 2004–10 Thailand, 2000–09 Moldova, 2001–10 Romania, 2001–09<br><!-- End of picture text --> At the same time, the state of agricultural modernization varies greatly across countries. Accordingly, in order to leverage the agricultural sector for labor‐intensive growth and poverty reduction, it is important to tailor policies to align them with a country’s context. The 2008 WDR provides a useful typology:

> 14 The 2008 World Development Report _Agriculture for Development_ calculates poverty based on an international poverty line of $1.08 in 1993 purchasing power parity dollars.

31
accounts for a very substantial share of the growth in exports from the CEECs to the EU (Table 5).

In contrast, it plays a very minor role in MENA, the two exceptions being Morocco and Tunisia.

**Table 4: Exports After Outward Processing (Share in Total exports to** EU)

<!-- Start of picture text --> CEEC MENA<br>1989 1993 1989 1993<br>Total 10.4 17.9 1.6 1.7<br>Leather 38.9 34.5 8.0 8.5<br>Garments 60.8 74.5 15.6 11.1<br>Machinery 8.1 14.4 5.4 2.6<br>Transport 12.3 4.7 4.5 2.3<br>Instruments 6.4 11.9 6.5 2.5<br>Furniture 26.5 13.9 1.2 1.5<br><!-- End of picture text --> ource: EUROSTT, **CtMEXT** database.

**Table 5: Exports to EU Under Outward Processing Regime**

||**Total 1993**|**Share of OPT in absolute increase in exports to**|
|---|---|---|
|l______________|**(ECU million)**|**EU, 1989-1993**(%)|
||6 CEECs|3,600|26l|
||MENA|**_500_**|4|

Source: EUROSTAT, COMEXT database.

The data reveal clearly that the CEECs are well on the way to exploiting their geographic proximity to the EU, which in conjunction with their relatively low wages and significant stocks of human capital makes them formiidable competitors for the MENA region. The geographic advantage that the MENA region used to have-because Eastern Europe was effectively closed to open exchange with the West--has now disappeared. MENA must now compete head-to-head with the CEECs. This is indeed a challenge, not least because relative labor costs in the CEECs and MENA are not that different. Per capita incomes in the CEECs--one proxy for such costs--are close to MENA levels (Table 6). The fact that Eastern Europe is able to exploit sub-contracting of manufacturing products 21
# **Reference**

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21
Table 3 – Key findings of Landscape studies

|**Region**|**SS Africa**|**LAC**|**Asia**<br>**Oceania**|
|---|---|---|---|
|**No. of countries evaluated**|51|20|31|
|**No. countries with MI**|39|19|24|
|**No. providers**|511|99|216|
|**No. products**|598|159|500|
|**No. lives or properties covered - millions**||||
|**Life and credit life**|42.7|48.3|83.9|
|**Health**|2.4|10.2|27.9|
|**Accident**|2.0|23.9|77.8|
|**Property**|0.8|2.9|7.7|
|**Agriculture**|0.2||26.2|
|**Takaful**|||4.7|

Detailed country level data are provided by the studies. Currently the largest commercial microinsurance markets in terms of percentage coverage are South Africa, Tanzania, Ethiopia, Mexico, Brazil, India, Bangladesh, the Philippines and Thailand, with South Africa and the Philippines having the largest penetrations. If government sponsored arrangements were added the numbers would increase dramatically, especially in the cases of India and China.

# **The main challenge for microinsurance**

A number of high level papers (Joint NGO briefing paper on health insurance, 2008; and Ekman, 2004 on community based health insurance; Smith and Watts, 2009; and Skees et al., 2007 on Index based agricultural insurance) discuss the scalability of health and index based microinsurance programs. These are not research papers based on any rigorous methodology and tend to be descriptive. However they do raise the important issue of whether microinsurance can be scaled up to cover more than the relatively small audiences reached to date by most pilots.

Thom et al (2014) review 95 microinsurance initiatives that have achieved scale. Their summary of findings reads as follows: ‘Most initiatives that achieved scale in microinsurance did not build up to scale over time. Rather they sought out and maintained access to large target groups either through mandatory sales, partnerships (including access to voluntary groups, branding and product design) as well as agency. These were found to be the main drivers of scale that microinsurance schemes could control. Government subsidy and latent demand also significantly increased uptake as external drivers of scale beyond the control of microinsurance schemes. Technology, management capacity, regulation and infrastructure were found to be significant enablers, not directly driving uptake of the products but critical to support sustainable scale.’ There have been some major successes where governments, insurers and development organizations have worked together. These include crop insurance in India where 9 million farmers are covered (see Clarke et al (2012)) and mutual health insurance in Rwanda where 90% of the population is covered (see Binagwaho (2012)). These examples still have outcome issues but do suggest a possible way forward.

21
(4.08) and Papua New Guinea (3.98). The mean value of _SXP_ equals 2.13 and the standard deviation is 1 (Table 2).

To check for the robustness of our results, we use a number of alternative measures of natural resource abundance discussed in the literature. We report detailed results on two of these measures: the (log of) primary exports expressed as a percentage of total merchandise exports, averaged over 1998-2002 ( _PX_ ); and the (log of) share of mineral production in GDP in 1971 ( _Minerals_ ). The _Minerals_ variable is taken from Sachs and Warner (1997) and was originally constructed using country-specific production data from the U.S. Bureau of Mines for the top twenty-three minerals in 1971.

The three measures of resource abundance show substantial correlation with each other. For example, _SXP_ shows a correlation of 0.71 with _PX_ and 0.49 with _Minerals_ . Results using two other measures, _net_ primary exports to GDP ratio and total land area per person, are discussed in the robustness section.

# _Other determinants of reform_

The proclivity to reform is likely to depend on the quality of the broader economic institutions that may determine how governments behave. Further, a number of studies also show that the quality of institutions is inversely correlated with the abundance of natural resources (see, for example, Auty 2001). As some of these studies suggest, greater resource abundance may be a causal factor for the poorer quality of institutions that may in turn lead to a lower incidence of reforms. Nevertheless, we control for a number of alternative proxy measures of broader economic institutions suggested in the previous literature to ensure that our results are on the conservative side.

7