rafmacalaba/datause-encoder-data huggingface dataset:
self-employment activities that have traditionally been the focus of attention. Foster and Rosenzweig (2003 and 2004) analyze NCAER data from roughly 250 villages covering the period 1971, 1982 and 1999 to study the evolution of the non-farm economy in rural India. These data permit the authors to calculate non-farm incomes, and they show that non-farm income shares have increased significantly during this time period.<sup>6</sup> Foster and Rosenzweig suggest that a growing rural based export-oriented manufacturing sector can be expected to have an important pro-poor impact in rural India, possibly more significant than that which can be expected from agriculture-led growth. This follows from their observation that rural diversification tends to be more rapid and extensive in places where agricultural wages are lower and where agricultural productivity growth has been less marked. Although the Foster and Rosenzweig study employs different data definitions and conventions than NSS-based studies (including the present study) their evidence is suggestive of a very significant rise in non-farm employment shares during their study period. They suggest that by 1999 about 44% of males aged 25-44 had primary employment in the non-agricultural sector by 1999. These figures appear higher than what NSS data suggest (although the figures cannot be directly compared as they refer to different age groups and different employment definitions). An important question is whether expansion of the non-farm sector observed by Foster and Rosenzweig occurred steadily during their reference period, or whether it took place in fits and starts. As others have already suggested, and we shall see further below, NSS data indicate that between 1987/8 and 1999/0 there was little expansion of non-farm employment in rural areas.

A recent study by Eswaran, Kotwal, Ramaswami and Wadhwa (2007) pursues the question of whether expansion of the non-farm sector is likely to influence agricultural wage rates. This could be an important route through which rural non-farm employment influences rural poverty, particularly if, as suggested by Foster and Rosenzweig (2003 and 2004) non-farm enterprises have been establishing themselves in areas where agricultural wages are low (and therefore rural poverty is high). The analysis by Eswaran

> 6 Foster and Rosenzweigh (2003b) suggest that non-farm income shares grew from just under a third in 1982 to nearly 50% in 1999.  A study by Lanjouw and Shariff (2002) based on a different NCAER dataset for 1993 calculated a rural non-farm income share of 37%.  This is suggestive of steady growth of the nonfarm sector throughout the 1980s and 1990s – a trend which NSS employment data do not appear to corroborate (see below).

9
The factor composition of costs In a cost function approach, observed total cost would be: Total Cost = f(output levels, factor prices) In turn, output levels include at least the number of workers who receive collection and record-keeping services, the insurance coverage produced, and the volume of pension fund management services. Finally; charges would be determined from costs and the factors that determine profit margins, such as market structure, barriers to entry, the degree of product differentiation and conduct.

It is useful to discuss briefly how total costs depend of different pieces of the pension services package.

Investment management costs are less than proportional to wealth and income because there are economies of scale in some of these services. This is shown by costs studies of the mutual fund management industry in the U.S., which find cost- assets elasticities between 0.423 and 0.871 (Baumol et al, page 187) More importantly, investment management costs depend of the type of portfolio held. They are very low in Malaysia, where 78% of the funds are held in specially-issued treasury securities and 10% is held in short term notes issued by local banks, several of which are state-owned. Investment management costs have been small in Chile, where investment regulations have limited asset choice. In March 1992 Chilean pension fund managers could choose only among 48 equities, 38 corporate bonds and 37 bank issuers. In the United States, where asset choice is -very wide, investment management costs are large. However even in this case investment management costs are less than a quarter of total costs (see evidence in Table 8). The tendency towards indexed funds and other types of passive management should counteract the higher costs due to the tendency towards global investment, suggesting that investment management costs will remain secondary.

Equipment costs and working capital costs are not dominant in the pension administration industry, as seen in the relatively low value of directly-owned assets. For example, based on 1991 accounting data for fixed and circulating assets, the three largest Chilean pension fund managers needed an average of 4.75 dollars per contributor per year to generate a
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33
<!-- Start of picture text --> by + + b,age, +b, age" + +b, age" + age" + + b, school, + p,sex, + bzhousshold,..3, + bzhousshold,..3, bzhousshold,..3, +, married, married,<br>+ $-child_under_S_yearg_old+ p,female, x married, + + b,female,<br>% children_count, + Vy ln (vail, Vy ln (vail, ln (vail, (vail, } 7 Vy, Solndustry_S DP, TU toservice_GDF,, 7 Vy, Solndustry_S DP, TU toservice_GDF,, Vy, Solndustry_S DP, TU toservice_GDF,, Solndustry_S DP, TU toservice_GDF,, DP, TU toservice_GDF,, TU toservice_GDF,, toservice_GDF,,<br>7 Uy Uy PeSME, ». gregiondummy, > » T,Ume_dummy, + ¢ gregiondummy, > » T,Ume_dummy, + ¢ > » T,Ume_dummy, + ¢ » T,Ume_dummy, + ¢ T,Ume_dummy, + ¢ + ¢ ¢ = U U<br><!-- End of picture text --> by + + b,age, +b, age" + +b, age" + age" + + b, school, + p,sex, + bzhousshold,..3, + bzhousshold,..3, bzhousshold,..3, +, married, married, + $-child_under_S_yearg_old+ p,female, x married, + + b,female, % children_count, + Vy ln (vail, Vy ln (vail, ln (vail, (vail, } 7 Vy, Solndustry_S DP, TU toservice_GDF,, 7 Vy, Solndustry_S DP, TU toservice_GDF,, Vy, Solndustry_S DP, TU toservice_GDF,, Solndustry_S DP, TU toservice_GDF,, DP, TU toservice_GDF,, TU toservice_GDF,, toservice_GDF,, 7 Uy Uy PeSME, ». gregiondummy, > » T,Ume_dummy, + ¢ gregiondummy, > » T,Ume_dummy, + ¢ > » T,Ume_dummy, + ¢ » T,Ume_dummy, + ¢ T,Ume_dummy, + ¢ + ¢ ¢ = U U In(wage,) = = ty + + age, + mage” mage” + a,school, + a,school, + + a,sex, + + By, In (val, (val, } = = 6. Yolndustry_G@DR,. “> Ba %service Ba %service %service GDR, - By %SME, + - By %SME, + By %SME, + %SME, + + » yreglon.dummy, yreglon.dummy, > y é,time_dummy, é,time_dummy, <!-- Start of picture text --> In(wage,) = = ty + + age, + mage” mage” + a,school, + a,school, + + a,sex, + + By, In (val, (val, } = = 6. Yolndustry_G@DR,.<br>“> Ba %service Ba %service %service GDR, - By %SME, + - By %SME, + By %SME, + %SME, + + » yreglon.dummy, yreglon.dummy, > y é,time_dummy, é,time_dummy,<br><!-- End of picture text --> <!-- Start of picture text --> (= {2,3,4))<br><!-- End of picture text --> <!-- Start of picture text --> (= (2004,2005,2006,2007})<br><!-- End of picture text --> (= {2,3,4)) (= (2004,2005,2006,2007})
uncovered by formal pension schemes, reaches retirement with significant potential resources to partially maintain their levels of consumption in old age.

Overall, elderly Pakistanis are in fact less likely to live in a poor household, compared to non-elderly adults (Table 4). The ratio appears stable or to slightly go down over time, indicating that elderly Pakistanis have benefited from the reductions in poverty of the 2000s as much or perhaps more than the general population. This is also consistent with the fact that we documented earlier: wealth at age 60-65 has increased significantly over the period of our sample.

_Table 4 Economic characteristics of elderly Pakistanis_

||Hous<br>(c|ehold Net W<br>ons. Adjuste|orth<br>d)||||||Non-|Non-|
|---|---|---|---|---|---|---|---|---|---|---|
||1st<br>Quartile|Median|3rd<br>Quartile|Elderly<br>poverty<br>ratio<sup>1</sup>|Co-<br>residence<br>rate<sup>2</sup>|Res.<br>Wealth/net<br>worth (%)<sup>3</sup>|Net<br>worth ><br>6mo.(%)<sup>4</sup>|Net worth<br>><br>60mo.(%)<sup>4</sup>|resid.<br>wealth ><br>6mo.(%)<sup>4</sup>|resid.<br>wealth ><br>60mo.(%)<sup>4</sup>|
|Year|||||||||||
|2001|14.3|36.8|91.6|57.0|84.0|34.5|50.9|24.9|0.91|95.0|
|2005|20.7|51.4|111.7|67.6|91.9|45.1|56.9|27.4|0.91|92.8|
|2007|25.2|59.7|127.8|55.2|93.2|49.9|58.8|29.9|0.84|94.0|
|2010|17.9|46.8|105.4|54.9|87.9|42.4|53.4|24.2|0.82|94.3|
|2011|20.9|49.9|112.3|59.6|91.5|43.6|54.1|25.8|0.90|94.0|
|2013|22.5|55.3|118.9|57.9|90.9|47.2|55.0|27.2|0.77|95.1|
|2015|26.6|57.0|120.8|59.2|91.7|47.9|53.1|25.6|0.78|94.6|
|2018|24.5|61.6|120.6|60.8|91.4|50.8|53.5|24.1|0.84|95.0|
|Avg.|21.6|52.3|113.6|59.0|90.3|45.2|54.5|26.1|0.85|94.3|

1. Elderly poverty rate: percentage of elderly Pakistanis living in households below the national poverty line over the percentage of households with non-elderly household head under the national poverty line.

2. Co-residence: percentage of elderly individuals living in type 1 and type 2 households.

3. Res. wealth/net worth: residential wealth as percentage of total wealth of households (average).

4. Net worth (or Non-resid. Wealth) > 6 (or 60) mo.: percentage of households that have net worth (or non-residential wealth) greater than 6 (or 60) months of the household’s consumption needs.

# **5. Decomposition methodology**

Our goal is now to measure how average household net worth evolves along the life cycle, but using a single cross-section of data (i.e. from one specific year) severely understates household net worth accumulation. To see this, Figure 1 plots household net worth, measured in multiples of monthly household consumption, against the age of the household head for the 2001, 2010 and 2018 PSLM survey rounds. Age profiles thus constructed appear to increase by around 1 month of consumption per year. However, this fails to consider that each cohort is wealthier than the next: the 2018 cross-sectional profile is shifted upwards by around 20 months of consumption relative to the 2001 profile.

10
considering the top posts in both politics and banking. Most likely what we are finding here is the tip of the iceberg.

# **3. Results**

In this section we relate the information on what banks have politicians in their board, and what politicians become bankers. We also explore how the country-measures of prevalence relate to various bank, politicians, and country characteristics. The aim is to shed light on the causes and consequences of the banker-politician phenomenon and determine whether it is more likely to be related to the public or private interest hypothesis. Our empirical approach is based on reduced-form evidence: we document a series of correlations and contrast them with the ones predicted by different explanations. When taken together, the different pieces of evidence turn out to be quite supportive to the regulatory capture story.

## **3.1. What Politicians Become Bankers?**

To address this question we define each politician as the unit of observation, and explore the variables that correlate with politicians being also bankers. Our basic data, described above, are complemented with political data from Polity IV.

Politicians that become bankers are different from those that don’t across two dimensions that can help disentangling the different hypotheses. First, politicians in charge of finance and economic functions and central bankers are much more likely to sit on the board of banks (Table 2, Panel A). On the other hand, ministers in charge of politics and public services such as health and education are significantly less prone to be also bankers. These results can be interpreted in different ways. The fact that banker-politicians seem to be on cabinets that require similar skills to those needed to run a bank supports in principle the public interest view. On the other hand, under the private interest hypothesis the larger the influence of the politician on the regulation of the sector, the better for both the banker and the politician. This would make it much more attractive to offer a board seat to someone related to these regulatory matters.

Interestingly, we find that the supervisors of banks, insurance companies, and securities, are no more likely to be also bankers than the average politician. In the light of the discussion above, this would mean that supervisors are either not influential enough or not knowledgeable enough on these matters. Although one can image it either way depending on the country, we feel more inclined to think that these typically lower-rank, less political and more technical officers simply lack the power to radically change the size and distribution of rents in the industry. We 8
minority women, the literacy gap with respect to men in the reference group is equally large, at 20.3 percentage points. These impacts, however, are based on a much lower base given that only two in five people 12 years of age and above are literate in Sierra Leone. Nevertheless, genderbased differences are large when evaluated in relative terms, since female literacy rate in Sierra Leone is below 30 percent for women 12 years of age and above, but nearly 50 percent for men.

# **_4.2. Primary School Completion_**

As discussed earlier, the educational attainment variable in the IPUMS-I data set is a harmonized variable with four ordered outcomes. To understand the role of gender, ethnicity, and the intersection of the two on these outcomes, we conduct an ordered probit and report average marginal effects for primary school and secondary school completion. We supplement this analysis with a standard probit, using a simplified version of these two outcome variables (full regression results are provided in Appendices B and C).

In the ordered probit results, we find that ethnicity has the largest absolute impact on primary school completion in Mexico, where ethnic minority men are 8.9 percentage points less likely to fall into the “completed primary school” category than Spanish-speaking men (Table 4). Following the literacy findings discussed above, gender has the strongest impact in Sierra Leone, where women in the reference group are 13.5 percentage points less likely to fall in the “completed primary school” category than males in the same group. Interestingly, some coefficients of interest are positive for Peru, where primary school completion rates are close to 70 percent, and Sierra Leone, where primary school completion rates are just over 20 percent. In particular, women and ethnic minorities in Peru are _more likely_ to fall into the “completed primary school” category than into any other category. In Sierra Leone, the exact opposite holds. Women and ethnic minorities are less likely to fall in the “completed primary school category”, but being ethnic minority women has a small positive impact on this likelihood.

We consider two possible explanations that may be underlying these results. On one hand, policies that encourage female and minority school enrollment may tip the scale to the advantage of these groups, and in fact, it is well documented that school enrolment among girls has surpassed that of boys in several Latin American and some African countries (Tembon and Fort, 2008). On the other hand, it is possible to observe a positive coefficient for women and 19
|**Table A20: iJET Country Secu**<br>**Data Provider**<br>iJET|**rity Ri**|**sk Rat**|**ings (**|**IJT)**|||||
|---|---|---|---|---|---|---|---|---|
|**Description**<br>Commercial security risk consulting co<br>**Website**<br>www.ijet.com<br> <br>|mpany|based i|n Anna|polis,|United|States|||
|**Data Source**<br>Country Security Risk Ratings|||||||||
|**Type**<br>Expert assessment|||||||||
|**Respondents**<br>iJET staff|||||||||
|**Frequency**<br>Annual since 2004|||||||||
|**Coverage**<br>Global sample of countries|||||||||
|**Public Access**<br>Commercially available|||||||||
|**Description**<br>iJET provides assessments of security r|isks fac|ed by t|raveler|s, code|d on a 5|-point|scale.||
|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|**Voice and Accountability**|||||||||
|NA<br>..|..|..|..|..|..|..|..|..|
|**Political Stability and Absence of Violence**|||||||||
|Security Risk Rating<br>X|X|X|X|..|..|..|..|..|
|**Government Effectiveness**<br>NA<br>..|..|..|..|..|..|..|..|..|
|**Regulatory Quality**<br>NA<br>..|..|..|..|..|..|..|..|..|
|**Rule of Law**|||||||||
|NA<br>..|..|..|..|..|..|..|..|..|
|**Control of Corruption**|||||||||
|NA<br>..|..|..|..|..|..|..|..|..|
|**Country Coverage**<br>**184**|**182**|**177**|**167**|**..**|**..**|**..**|**..**|**..**|
|**Year of publication**<br>**2007**|**2006**|**2005**|**2004**|**..**|**..**|**..**|**..**|**..**|

58
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 8. Access to piped water and improved sanitation is higher in urban areas**

## a. Water

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>piped supply public wells/boreholes surface water<br>standposts<br>urban rural<br>b. Sanitation<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>septic tank improved latrine traditional no facility<br>latrine<br>urban rural<br>% of population<br>% of population<br><!-- End of picture text --> Source: Banerjee and others 2008.

# **Challenges**

The vast majority of the country’s population still relies on unimproved sanitation services. **C** overage of flush toilets is extremely low (5 percent in the Republic of Congo versus 11 percent in the benchmark group), and traditional latrines remain the main source of sanitation in both urban and rural areas. At the same time, the country has achieved good progress in reducing open defecation by increasing the use of traditional latrines. Only 10 percent of the population practices open defecation, compared with 28 percent for other resource-rich African countries.

24
# **2.2 For most countries, food expenditures remain above 50 percent of total expenditures for 60 percent of the population or more**

Disaggregating expenditures by country shows great variation in spending across countries and country quintiles. Figures 3 to 5 show household expenditures by country and quintile, ordering the countries by GDP per capita.<sup>10</sup> In terms of food expenditures (see Figure 3) most countries follow a regular pattern of declining shares of food expenditures as income quintiles increase. Country-level housing expenditures follow a less regular pattern across quintiles. In Kenya, Senegal, Tanzania, and to a lesser extent Angola (see Figure 4), shares of housing expenditures increase with quintiles, whereas in Cameroon and Malawi shares of housing expenditures fall with income quintile. The remaining countries have an irregular pattern or flat housing expenditures, which likely owes to the fact that because incomes are quite low, the percentage of income allocated towards housing may not increase substantially with income levels. This may also be linked with increases in housing quality across income quintiles, which can also affect housing expenditure allocations.

Transportation expenditures, on the other hand, are much more consistent across countries with transport expenditures increasing for the higher quintiles of the expenditure distribution (see Figure 5). The only country to betray the pattern of increasing transportation expenditures is the island nation of Comoros, which exhibits an unclear relationship between transportation expenditure and income quintiles. One explanation is that transportation costs in Comoros are nearly 2.5 times the Africa region average, making it the most expensive of any African country (African Development Bank 2012). Unlike spending patterns for food, there appears to be no relationship between GDP per capita and country-level spending patterns for housing or transportation.

**Figure 3. Food Expenditures by Country and Quintile, in Ascending Order of GDP per Capita— Urban Areas**

<!-- Start of picture text --> 80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>I III V II IV I III V II IV I III V II IV I III V II IV I III V II IV I III V II IV I III V II IV I III V II IV I III V II IV I III V II IV<br>NER ETH MWI LBR RWA UGA MOZ SLE TZA BFA KEN COM GHA ZMB SEN STP CIV CMR NGA AGO<br>food expenditures as % of total<br><!-- End of picture text -->

> 10 The sequencing of GDP is determined by income data from the World Development Indicators (WDI) 2012 and represents GDP per capita with purchasing power parity in constant USD.

6
Table 4. Brazil’s Antidumping Filings by Industry <u>(1996–1999)</u>

||Three-digit ISIC industry|Number of<br>cases|
|---|---|---|
|311|Food products|5|
|313|Beverages|0|
|314|Tobacco|0|
|321|Textiles|3|
|322|Apparel except footwear|0|
|323|Leather products|0|
|324|Footwear except rubber or plastic|0|
|331|Wood products except furniture|0|
|332|Furniture except metal|0|
|341|Paper and products|0|
|342|Printing and publishing|0|
|351|Industrial chemicals|27|
|352|Other chemicals|3|
|353|Petroleum refineries|0|
|354|Miscellaneous petroleum and coal products|0|
|355|Rubber products|6|
|356|Plastic products|0|
|361|Pottery, china, earthenware|0|
|362|Glass and products|1|
|369|Other non-metallic min. products|4|
|371|Iron and steel|15|
|372|Non-ferrous metals|0|
|381|Fabricated metal products|2|
|382|Machinery except electrical|1|
|383|Machinery electric|0|
|384|Transport equipment|0|
|385|Professional and scientific equipment|2|
|390|Other manufacturedproducts|1|

_Source_ : Bown (2010) 21