rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 120.00<br>110.00 —<br>—_— 2 a<br>100.00 -— #.<br>7 —7~<br>90.00 a= 7<br>og 7<br>—- %<br>80.00 - es<br>60.0070.00 -— LZ<br>50.00 ~ : r —<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>=— Trade(SIC6) —=Trasport(SIC7)= <=<=Finance (SIC8) ——Community (SIC 9)<br><!-- End of picture text -->
# **3.5 Demographic and Health Surveys**

The Demographic and Health Surveys (DHS) are nationally representative household surveys on fertility, health, and family planning behaviors and attitudes, collected in over 90 countries, and administered by host country governments with technical assistance from ICF International and other agencies under the MEASURE project.<sup>13</sup> Since 2009, they have included questions about land and housing ownership.

All DHS surveys include a minimum of two questionnaires—a Household Questionnaire and a Women’s Questionnaire. The Household Questionnaire can be administered to any capable member of the household age 15 years or older and collects information on individual and household characteristics. All women of reproductive age (15-49) that are identified in the household roster are eligible to respond to the Women’s Questionnaire. In addition, many surveys include a Men’s Questionnaire, generally administered to men (age 15-49/54/59, depending on the country). In some cases, the sample only includes ever or currently married men and women.

The DHS sample sizes vary widely across countries and years, but are generally between 5,000 and 30,000 households, and are based on calculations of the optimal sample size for two-stage cluster sampling in each country. Due to the structure of the survey, more women are interviewed than men. The DHS are typically conducted every five years to facilitate analysis of changes over time.

Questions are asked both about household landholdings and about whether the respondent is an owner. For both land and housing, the respondents are typically asked, “Do you own any land either alone or jointly with someone else?” and responses of no ownership, sole ownership, joint ownership or both sole and joint ownership are allowed.

Thus, the unit of analysis is the respondent and the survey identifies irrespectively of whether he or she is a landowner. A similar question is asked about housing. If an individual identifies as a joint owner, the other owner is not identified. No information is collected on the value or area of the land or housing.

# **3.6 World Programme for the Census of Agriculture**

The World Programme for the Census of Agriculture (WCA), housed at the Food and Agriculture Organization of the United Nations (FAO), provides methodological guidelines and technical assistance for country agricultural census operations. The WCA 2010 round was undertaken from 2006 to 2015, and was centered around a modular

> 13 For more information, please visit: <u>http://www.measuredhs.com.</u>

14
skewed between regions within the country. However, since neither factor was included in the earlier Wodon (1999) study of the micro-determinants of poverty reduction we cannot make direct comparison between our results and those from the past.

Looking ahead, what do the findings of this paper imply for policies to sustain and improve the pace of poverty reduction? Improving labor productivity in agriculture would be critical to raise earnings of agricultural wage workers who have a high incidence of poverty. Given the population pressure on land, achieving higher agricultural labor productivity would require accelerated growth in the non-agricultural sectors to absorb workers from low return agricultural wage employment. The relatively high returns to non-agricultural self employment underscore the importance of this sector for poverty reduction. The rise in returns from and growth of household-based nonfarm enterprises may be linked to the rapid spread of microfinance. Further improving the access to finance for small enterprises, particularly in urban areas where microfinance is less prevalent, is likely to spur their growth.

A fall in dependency ratios within households played a key role in reducing poverty between 2000 and 2005, indicating that sustaining Bangladesh’s past successes in reducing fertility is crucial for poverty reduction. Raising education attainments will also have high dividends in terms of higher earnings and reduced poverty. This paper shows the clear link between household welfare and education of _all_ household members, and not just that of the household head. As women’s participation in the labor force increases, there are increasing economic benefits of women’s education to the household – to complement the social and intra-household benefits associated with women’s education.

22
24 line income and their income.<sup>26</sup> Who would gain/lose from such a subsidy design, and how much?

Table 19 summarizes the results from this exercise. There are a total of four groups of households. The first does not receive the HUS at present, nor does it receive the redesigned version. The loss of surplus for these households would be zero. Then there are households that do not currently get the HUS, but would under the new proposal. This group represents only 3.50% of the households, and would stand to gain from the proposed overhaul of the HUS (on average 1,769 UAH per household per year, in 2014 UAH). The next group (more than half of the sample) loses the HUS under the new proposal, losing on average 2,240 UAH (2014 UAH) per year. Finally, about 10% of the sample would continue to receive the HUS, although the new amount may well differ from the original one. Indeed this group includes just as many losers as it does winners, for an average CS change of -4 UAH (2014 UAH). Overall, the proposed subsidy design brings a loss of CS in the amount of 1,140 UAH per household per year. When attention is restricted to previous HUS holders, the CS loss is 1,689 UAH per household per year (2014 UAH).

Table 20 examines the impact of the redesigned HUS by income and consumption quartile. Each cell represents an income-consumption quartile pair, and the panels on the left of the table display how the households that belong to one such pair would be distributed among the four groups listed in table 19. Hence, the percentages in a given cell (e.g., the one for first quartile of income and first quartile of consumption) sum to 100% across the four panels. People

> 26 <mark>The poverty line is 1,795.6 UAH monthly per adult-equivalent in 2016, 2,128.597 in 2017, and 2,325.0 UAH in 2018. One adult-equivalent is 1+0.7 × (household size - 1). The statutory subsistence minimum (SM) is the official poverty line used in Ukraine. Households with per capita incomes below the statutory SM are considered poor and are eligible for a variety of social assistance programs. The statutory SM is a policy tool set by the Council of Ministers. See http://www.ukrstat.gov.ua/, tabs “Statistical Information,” “Income and living conditions,” “Differentiation of household living standards.”</mark>
> <sup>- 13 -</sup>

the second and third pillars were close to 6 percent of GDP or about 10 percent of earnings.

**<u>Table 5 Contributions to Personal Pension Plans, 1995-2004</u>**

||**1995**|**2000**|**2004**|
|---|---|---|---|
|Contributions (DKKbillion)|15.584|16.209|19.933|
|Contributions/GDP(%)|1.54%|1.25%|1.36%|
|Contributions toAnnuities (% oftotal)|11.8%|15.2%|15.1%|
|Contributions toPhased Withdrawals (%)|20.2%|37.6%|49.3%|
|Contributions toLump Sums (%)|68.1%|47.3%|35.6%|
|Contributions toL&Pcompanies (%)|40.8%|44.2%|41.7%|
|Contributions toBanks (%)|59.2%|55.8%|58.3%|
|Contributions to Unit-Linked||3.2%|6.5%*|

* 2003 Source: Statistics Denmark, Central Customs and Tax Administration and the Danish Insurance Association.

Contributions to annuities (life or term) have absorbed a small part of personal pension contributions at close to 15 percent. As in the case of second pillar contributions, there has been a major shift away from lump sum payments and in favor of phased withdrawals. This reflected changes in the tax treatment of lump sum payments. Banking institutions play a bigger part in personal pension plans than in second pillar plans, accounting for nearly 60 percent of total contributions. The relatively greater success of banks in personal pension plans may be explained by their stronger presence in the retail financial services market and the preference of some people for savings rather than insurance.

Members of personal pension plans are subject to health screening if they purchase any insurance products, while annuity prices are not based on unisex rates as is the case with occupational schemes. However, personal pension plans often benefit from guaranteed minimum investment returns that are similar to those offered in occupational schemes. The recent reduction in guaranteed returns has stimulated a growing interest in unitlinked products, and greater demand for individual choice. Contributions to unit-linked products reached 6.5 percent of the total in 2003. Unit-linked products benefit some times from a guarantee against negative returns by giving up some of the upside potential.

# **D.   Asset Accumulation and Investment Performance**

With expanding coverage, rising contribution rates (as suggested by the collective labor agreement between LO and DA) and positive real investment returns, the total assets of the pension system increased from DKK 847 billion in 1995 (corresponding to 83 percent of GDP) to 1,822 billion in 2004 (125% of GDP). This is comparable to the level found in Switzerland, the Netherlands and the UK and suggests the existence of a strong capital base for financing the pensions of retiring workers over the coming decades.
||**Ta**|**ble 4: Additi**|**onal controls**||||
|---|---|---|---|---|---|---|
|Dependent variable:<br>Financially Constrained<br>(dummy)|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Incidence of Petty|0.779**|0.842**|0.899**||||
|Corruption (Cell average)|(0.336)|(0.333)|(0.350)||||
|Depth of Petty Corruption<br>(Cell average)||||0.982***|1.053***|1.036***|
|||||(0.363)|(0.363)|(0.381)|
|Annual Sales (2009 USD,|-0.128***|-0.126***|-0.374***|-0.128***|-0.126***|-0.374***|
|logs)|||||||
||(0.038)|(0.038)|(0.079)|(0.038)|(0.038)|(0.079)|
|Age of Firm (logs)|-0.046|-0.043|-0.016|-0.050|-0.048|-0.021|
||(0.088)|(0.089)|(0.096)|(0.089)|(0.089)|(0.096)|
|Exports (proportion of|<br>-0.855**|<br>-0.839**|<br>-0.603|<br>-0.848**|<br>-0.830**|<br>-0.590|
|sales)|||||||
||(0.384)|(0.387)|(0.393)|(0.385)|(0.389)|(0.394)|
|Firm Has Female Owners<br>Y:1 N:0|-0.247**|-0.233**|-0.255**|-0.245**|-0.232*|-0.254**|
||(0.118)|(0.118)|(0.126)|(0.118)|(0.119)|(0.126)|
|Foreign Ownership|0.156|0.154|0.138|0.150|0.145|0.129|
|(proportion)|||||||
||(0.303)|(0.306)|(0.321)|(0.305)|(0.307)|(0.323)|
|Multi-establishment Firm<br>Y:1 N:0|-0.148|-0.160|-0.248|-0.148|-0.161|-0.247|
||(0.201)|(0.203)|(0.227)|(0.200)|(0.202)|(0.226)|
|Firm Purchased Assets Last|-0.591***|-0.597***|-0.518***|-0.594***|-0.600***|-0.519***|
|Fiscal Year Y:1 N:0|||||||
||(0.107)|(0.106)|(0.120)|(0.106)|(0.105)|(0.120)|
|Firm is Audited Y:1 N:0|-0.054|-0.054|-0.064|-0.058|-0.057|-0.068|
||(0.125)|(0.125)|(0.139)|(0.125)|(0.125)|(0.139)|
|Hours of Power Outages in<br>a Month|0.000|0.000|0.000|0.000|0.000|0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Firm Experienced Losses<br>from Crime Y:1 N:0|-0.542***|-0.536***|-0.498**|-0.543***|-0.536***|-0.495**|
||(0.205)|(0.203)|(0.222)|(0.204)|(0.202)|(0.221)|
|Firm Inspected/Visited by<br>Tax Officials Y:1 N:0|-0.164|-0.155|-0.202|-0.163|-0.155|-0.201|
||(0.118)|(0.118)|(0.125)|(0.118)|(0.118)|(0.125)|
|Informal Competition|0.433|0.403|0.342|0.449|0.417|0.361|
|<br>(average)|||||||
||(0.330)|(0.328)|(0.317)|(0.328)|(0.326)|(0.316)|
|How Much of an Obstacle?:<br>Courts (average)|0.372***|0.503***|0.498***|0.384***|0.516***|0.503***|
||(0.135)|(0.150)|(0.157)|(0.136)|(0.150)|(0.158)|

39
# **<mark>Table 2: Input-Output Tables, 2004 and 2013</mark>**

|**a. 2**|**004 (in bil**|**lion 200**<br>|**4 cedis)**<br>|||||||
|---|---|---|---|---|---|---|---|---|---|
|||AGR|MIN|O-IND|WRT|O-SER|Intermediate<br>share of<br>output|Final<br>demand|Output|
|2004|AGR|0.077|0.000|0.117|0.016|0.000|0.211|32,513|41,195|
|<br>i|MIN|0.000|0.000|0.066|0.000|0.000|0.066|4,472|4,790|
|(n<br>2004|O-IND|0.031|0.036|0.201|0.025|0.070|0.362|14,141|22,174|
|<br>rices)|WRT|0.272|0.010|0.194|0.092|0.000|0.567|5,433|12,562|
|p|O-SER|0.070|0.011|0.017|0.129|0.252|0.479|20,915|40,135|
|**b. 2**|**013 (in bil**|**lion 201**<br>|**3 cedis)**<br>|||||<br>|<br>|
|||AGR|MIN|O-IND|WRT|O-SER|Intermediate<br>share of<br>output|Final<br>demand|Output|
|2013|AGR|0.136|0.000|0.068|0.018|0.041|0.263|30,944|41,959|
|<br>i|MIN|0.000|0.053|0.138|0.001|0.004|0.195|21,913|27,227|
|(n<br>2013|O-IND|0.039|0.026|0.099|0.145|0.080|0.388|45,247|73,906|
|<br>prices)|WRT|0.010|0.090|0.001|0.003|0.180|0.284|38,534|39,654|
||O-SER|0.072|0.019|0.042|0.073|0.119|0.325|38,703|57,364|

_Source_ : Authors’ elaboration based on 2004 and 2013 supply and use tables for Ghana (GSS 2006, 2021).

_Note:_ AGR = agriculture. MIN = mining. O-IND = industries other than mining; they include manufacturing, construction, and utilities. WRT = wholesale and retail trade. O-SER = services other than wholesale and retail trade; they include transport, communications, finance, commerce, government services, and private services.

23
**Table 4. Real per capita income growth by source of income, 2006 to 2012**

||**Crop**|**Livestock**|**Ag wage **|**Non-ag wage **|**Non-ag self**|
|---|---|---|---|---|---|
|**All households**||||||
|2006|179,796|81,643|132,723|92,701|237,554|
|2010|242,025|136,138|36,329|137,905|92,120|
|2011|241,297|134,695|32,154|138,208|106,895|
|2012|331,489|118,077|33,762|139,043|166,074|
|Annual growth, 2006-2012|9%|5%|-18%|6%|-5%|
|Annualgrowth,2006-2011|4%|7%|-18%|6%|-11%|
|**Bottom 40 percent**||||||
|2006|122,175|52,945|79,799|35,846|34,176|
|2010|206,352|101,512|36,678|59,297|70,004|
|2011|187,213|92,671|38,116|68,375|54,962|
|2012|300,687|110,956|35,757|59,550|80,611|
|Annual growth, 2006-2012|14%|11%|-11%|8%|13%|
|Annualgrowth,2006-2011|6%|8%|-10%|10%|7%|

Source: UNPS 2006-2012

**Figure 5: Regional differences in per capita crop income growth, 2006 to 2012**

<!-- Start of picture text --> 600,000<br>500,000<br>Central<br>400,000<br>Eastern<br>300,000 Northern<br>200,000 Western<br>100,000<br>0<br>2005 2009 2010 2011<br><!-- End of picture text --> Source: UNPS 2006-2012 To assess the role of different types of income growth in reducing poverty in Uganda we correlate the real per capita income aggregates with household consumption. This way, it is possible to ascertain whether growth in one of the income sources has been more important for increasing consumption than other income sources, particularly among the bottom 40 percent. Specifically, a fixed effects model was estimated using the log of per capita consumption and the log of per capita income (for each source of income), allowing an analysis of the relationship between changes in income and changes in consumption. Interview year and month fixed effects were also included. The analysis was conducted only for 2006 and 2010 as there is a marked reduction in the consumption aggregate after 2010 that is hard to explain and is inconsistent with the national poverty trends.<sup>9</sup> 9 One possible explanation is the methodological differences in the collection of consumption data in the 2011 and 2012 survey rounds. Computer Assisted Personal Interviews (CAPI) was introduced in the UNPS for the 2011 and 2012 rounds 13
57

|Country‐ Not in conflict|Eritrea|Kosovo|Serbia|Albani<br>a|
|---|---|---|---|---|
|Population (million)|5.1|1.8|7.1|2.9|
|GNI, Atlas $ bn|2.3|7.3|41.5|12.9|
|GNI per capita, PPP, $|1400|9300|13040|10980|
|Life Expectancy at birth in 2010|61|70|74|77|
|Life Expectancy at birth in 2014|64|71|76|78|
|Labor force participation rate in 2010 (%<br>of population ages 15+)|85|N/A|52|55|
|Labor force participation rate in 2014 (%<br>of population ages 15+)|85|N/A|53|55|
|Gross capital formation in 2000 (% of<br>GDP)|22|N/A|11|32|
|Gross capital formation in 2010 (% of<br>GDP)|9|33|18|30|
|Gross capital formation in 2014 (% of<br>GDP)|..|26|16|25|
|Individuals using the internet in 2010 (%<br>of population)|0.6|N/A|40.9|45|
|Individuals using the internet in 2014 (%<br>of population)|1|N/A|53.5|60.1|
|Merchandise trade in 2000 (% of GDP)|72|N/A|N/A|37|
|Merchandise trade in 2010 (% of GDP)|32|N/A|67|50|
|Merchandise trade in 2014 (% of GDP)|..|N/A|81|58|
|Electrification % of total population in<br>2013, *in 2012|32%|100<br>(in<br>2016)|100 (in 2016)|100 (in<br>2016)|
|Real GDP CAGR 2001‐2010 (years may<br>vary), %|0.9%|6.0%|4.4%|5.5%|
|Real GDP CAGR 2011‐2016 (years may<br>vary), %|*8.7%|3.0%|0.6%|2.1%|
||*2010‐<br>2011||||

Source: The World Bank Little Data Book 2016, IMF WEO, CIA World Factbook <u>References:</u> Altonji, J. G., & Card, D. (1991). The effects of immigration on the labor market outcomes of less‐skilled natives. In _Immigration, trade, and the labor market_ (pp. 201‐234). University of Chicago Press.

Aiyar, S., Barkbu, B., Batini, N., Berger, H., Detragiache, E., Dizioli, A., ... & Spilimbergo, A. (2016). The refugee surge in Europe. _Europe IMF Staff Discussion Note (SDN/16/02)_ .
STATISTICS AND ESTIMATES ON OUTPUT CONCENTRATION IN RUSSIA

**Table A11: Concentration of output**

|**YEAR**|**SHARE OFBOTTOM**|**SHARE OFTOP**|**TOTALOPERATING**|
|---|---|---|---|
||**QUARTILE OF SECTORS**|**QUARTILE OF SECTORS**|**REVENUE(IN**|
||**IN TERMS OF**<br>**OPERATING REVENUE**<br>|**IN TERMS OF**<br>**OPERATING REVENUE**<br>|**MILLIONUSD)**<br>|
|**1999**|0.4%|81.8%|45,700|
|**2000**|0.2%|90.7%|106,000|
|**2001**|0.6%|81.7%|76,300|
|**2002**|0.6%|79.2%|168,000|
|**2003**|0.6%|77.4%|190,000|
|**2004**|0.6%|78.3%|221,000|
|**2005**|0.6%|79.5%|234,000|
|**2006**|0.7%|81.0%|306,000|
|**2007**|0.7%|81.2%|394,000|
|**2008**|0.8%|79.9%|299,000|
|**2009**|0.7%|78.4%|259,000|
|**2010**|0.7%|78.7%|282,000|
|**2011**|0.7%|80.2%|278,000|

Source: Author’s calculations using RUSLANA

**Table A12: Concentration of output within sectors**

|**2 DIGITNACE 1.1**<br>**(MANUFACTURING**<br>**SUBSECTION) **|**AVERAGESHARE OF**<br>**BOTTOMQUARTILE**<br>**(WITHIN4-DIGIT**<br>**NACE1.1) OF FIRMS IN**|**SHARE OFTOP**<br>**QUARTILE(WITHIN4-**<br>**DIGITNACE1.1) OF**<br>**FIRMS IN TERMS OF**|**AVERAGE OFTOTAL**<br>**OPERATINGREVENUE**<br>**IN4-DIGITNACE1.1**<br>**(IN MILLIONUSD)**|
|---|---|---|---|
||**TERMS OF OPERATING**<br>**REVENUE**|**OPERATING REVENUE**||
|**Manufacture of food**<br>**products, beverages**|0.08%|93.6%|29,600|
|**Manufacture of textiles**<br>**and textile products**|0.14%|94.2%|4,160|
|**Manufacture of leather and**<br>**leatherproducts**|0.01%|96.1%|15,400|
|**Manufacture of pulp, paper**<br>**and paper products**|0.14%|94.6%|9,790|
|**Manufacture of coke,**<br>**refinedpetroleum**|0.0002%|99.5%|215,000|
|**Manufacture of chemicals,**<br>**chemical products**|0.02%|97.1%|13,800|
|**Manufacture of rubber and**<br>**plasticproducts**|0.13%|91.9%|11,100|
|**Manufacture of other non-**<br>**metallic minerals**|0.04%|91.9%|16,100|
|**Manufacture of basic**<br>**metals and fabrications**|0.07%|95.8%|19,200|
|**Manufacture of machinery**<br>**and equipment**|0.09%|94.3%|15,800|

29
**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

|**Study**|**Hypothesis**|**Dependent**<br>**Variable(s)**|**Independent**<br>**Variable(s)**|**Control Variables**|**Notes: # of countries, times**<br>**period, data source**|
|---|---|---|---|---|---|
||||agency) (RM, I)<br>_Results: significant at the 5%_<br>_level for both number of_<br>_privatisations and proceeds from_<br>_privatisation. Significant at the_<br>_10% level for the percentage of_<br>_privatised stock._|||
||_H3: regulation has a_<br>_positive impact on_<br>_aggregate proceeds of_<br>_privatisation._|-the per country aggregate<br>proceeds from total sales<br>(US$ 96) (PI)||||
||_H4: regulation has a_<br>_positive impact on the_<br>_percentage of privatised_<br>_stock._|-the stake privatised at firm<br>level (PI)||||
|**Steiner (2001)**|_H0: Regulation and_<br>_restructuring leads to_<br>_improved utilisation rate_<br>_and reserve margin in_<br>_electricity generation._<br>_H1: Regulation and_<br>_restructuring leads to lower_<br>_industrial electricity prices_<br>_and industrial/residential_<br>_price ratio._|-industrial end-user price in<br>PPPs (PI)<br>-ratio of industrial to<br>residential prices in PPPs<br>(PI)<br>-utilisation rate: energy<br>production/total average<br>capacity (PI)<br>-distance of actual from<br>optimal reserve margin<br>(PI)|**-**time to liberalisation (years)<br>(RM)<br>_Results: significantly positive for_<br>_prices_<br>-time to privatisation (years)<br>(RM)<br>_Results: insignificant for prices_<br>-unbundling of generation from<br>transmission (multi-level<br>indicator) (RM)<br>_Results: insignificant for prices,_<br>_significantly positive for_<br>_utilisation rate._<br>-private ownership (multi-level<br>indicator) (RM)<br>_Results: significantly positive for_<br>_prices and for utilisation rate._<br>-third party access (dummy)<br>(RM)<br>_Results: insignificant for prices_|-GDP (US$) (CL)<br>_Result: insignificant_<br>-hydro share in generation<br>(SE)<br>_Result: significant for_<br>_prices_<br>-nuclear share in<br>generation (SE)<br>-state preference against<br>nuclear technology (SE)<br>-state preference in favour<br>of coal technology (SE)<br>-urbanisation (CL)|Panel data from International<br>Energy Agency and other<br>sources covering 19 OECD<br>countries. Number of periods<br>(1986-1996): 11⇒number<br>of observations: 209|

58
have already begun when workers were in their mid-forties, but the trend grew more pronounced when they became superannuated or disabled. These factors created an incentive to find new methods of planning for old age. Most workers had the choice of raising a large family and relying on their offspring to take care of them or accumulating financial assets to live on in their later years. To avoid depending on family or charity, workers had to find new ways of accumulating assets while still at the peak of their earning capacity.<sup>2</sup> Many surveys taken before the turn of the century show that substantial numbers of workers reported saving more than 12 percent of their prime-year incomes. Overall, the national saving rate was close to 20 percent in the last decades of the nineteenth century.<sup>3</sup> These savings were sometimes invested in bank accounts, but more often were used to finance home ownership or invest in a variety of insurance funds. Hundreds of labor unions, employers, and benefit societies developed saving funds and insurance plans for the benefit of workers.

# Tontine **Insurance**

One type of popular policy sold by insurance companies was tontine insurance. A worker's premium payments to the insurance company were split with one portion covering the cost of simple term insurance and the remaining portion invested in a savings fund that accumulated interest over the life of the policy. The pool of accumulated assets was distributed only to the policy holders who survived to the end of the policy term. Thus if you died, your funeral was covered. If you lived, you received a lump sum payment that was much greater than what you could have earned from regular savings. This was a 2 2,Throughout the nineteenth century the traditional strategy of relying on the support of grown children had fallen from favor (Ransom, Sutch, and Williamson 1992). Behveen 1870 and 1940 the birth rate in the United States fell from forty-one to eighteen per 1,000. ;. The national saving rate estimate is based on the work of Gallman (1966). Our earlier work and that of other researchers with household budget data has documented the impressive saving rates that characterized late-nineteenth century industrial communities. See Ransom, Sutch, and Williamson (1992).

5