rafmacalaba/datause-encoder-data huggingface dataset:
> <sup>- 19 -</sup>

# **Figure 5  Market Concentration**

<!-- Start of picture text --> The concentration in the Danish market for life insurance companies and<br>Per cent pension funds<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2 largest 5 largest 10 largest 15 largest 20 largest 25 largest<br>1995 2000 2004<br>Source: The Danish Financial Supervisory Authority<br><!-- End of picture text -->

# **F.   Retirement Products**

Retirement products include various types of annuities, phased withdrawals and lump sum payments. A distinguishing feature of the Danish pension system is the use of deferred group annuities, whereby workers commit to a life annuity at the time of making their contributions at a preset minimum conversion factor that reflects the guaranteed minimum interest rate and the life tables. Annuity payments are augmented by bonus payments if the actual investment performance exceeds the guaranteed return or if the mortality experience is higher than expected.

This type of policy avoids the problems of adverse selection and provides guaranteed minimum benefits, but also allows participation in any future superior performance. However, its success depends on the equitable distribution of bonuses and requires strong confidence in the integrity of the management of pension institutions.

The benefits paid by the system also include survivor benefits to the heirs of contributing workers in cases of early death as well as disability pensions in cases of incapacitation. These types of benefits are based on calculations of actuarial probabilities based on past experience as well as future projections and expectations. The system also provides annuities with a minimum number of guaranteed payments as well as immediate life annuities, which are purchased at the time of retirement (or even later).

Group contracts are generally specified in collective labor agreements. These aim to provide for minimum annuity benefits to complement the pension benefits from the public schemes and also include survivor and disability benefits. Participating employees and their dependents are not subject to individual health screening but costs and benefits
# **All That Glitters Is Not Gold: Polarization Amid Poverty Reduction in Ghana**

Fabio Clementi<sup>1</sup> , Vasco Molini<sup>2,†</sup> , and Francesco Schettino<sup>3</sup>

> 1 _University of Macerata, Macerata, Italy_

> 2 _World Bank, Washington DC, USA_

> 3 _Second University of Naples, Naples, Italy_

**Keywords:** Ghana; consumption expenditure; poverty and inequality; polarization; relative distribution; decomposition analysis.

**JEL classification:** C14; D31; D63; I32.

> † Corresponding author. E-mail addresses: fabio.clementi@unimc.it (Fabio Clementi), vmolini@worldbank.org (Vasco Molini), <u>francesco.schettino@unina2.it (Francesco Schettino).</u> _Note_ : The authors acknowledge financial support from the World Bank. We thank Dan Pavelesku (World Bank) for excellent assistance with data preparation. We also thank Christoph Lakner, Pierella Paci and Andrew Dabalen (World Bank) for comments on an earlier version of the manuscript. Of course, we are the sole responsible for all possible errors the paper may contain.
Dx = x( _x_ t) - x( _x_ t-1) = x( _x_ t) - x( _xt_ ,t-1) + x( _x_ t,t-1) - x( _x_ t-1), (4) or in condensed form Thus, the total change in the inequality index of the nominal consumption vector _x_ (henceforth “nominal inequality” for short) can be broken down into a component DxQ that captures the effects of changing consumption _quantities_ (i.e. changing real inequality) and another component DxP that captures the effects of changing consumption _prices_ (i.e. inflation inequality). Notice that, holding constant real inequality, nominal inequality can change in response to price changes. In fact, if price changes are anti-poor then DxP < 0: prices rise faster at the lower tail of the distribution than at the upper tail, and hence for a fixed bundle of goods the relative spending of the poor rises. In such case, it follows that Dx < DxQ. Thus, _ceteris paribus_ , for given real inequality, anti poor price changes _reduce_ nominal inequality, giving the (false) appearance of an improving distribution. Similarly, anti-rich price changes imply DxP > 0 and, _ceteris paribus_ , lead to higher nominal inequality, giving the (again false) impression of a worsening distribution.

# **III. Empirical results**

# _III. 1 Data_

Inflation differentials across consumers are driven by two factors. The first is the difference in consumption patterns across individuals. The second is the difference in inflation rates across goods. Hence, for our empirical analysis we need two ingredients: data on the consumption patterns of different households, and data on the trends in the prices of different groups of goods. We take the data on consumption patterns from household expenditure surveys, which contain relatively detailed information on the type of goods and services consumed by different households. Depending on the country under consideration, consumption is disaggregated into 7 or 8 major categories, although finer partitions are usually possible. As for the matching price data, we rely on the disaggregation of the overall CPI by category of expenditure, which is usually available from national sources (typically the Central Bank).

Table 1 offers summary information on the expenditure surveys used in the empirical analysis. The surveys correspond to four Latin American countries and allow constructing nine inflation spells: one each for Brazil (1988-1996) and Colombia (1997-2003), four for Mexico (1984-1989, 1989-1994, 1994-1996, and 1996-2002) and three for Peru (1995-1999, 1999-2001, and 2001-2003). The nine spells span periods of time of varying length: 8 years in Brazil, 6 years in Colombia, between 2 and 6 years in Mexico (for a grand total of 18 years), and between 2 and 4 years in Peru (for a combined total of 8 years).

Tables 2 to 5 report for each country the budget shares devoted by each quintile of the expenditure distribution to the various categories of goods and services: apparel, education, food 5
by female candidates in a given district by the share of seats that women won against men in a close election. We use data from the 2015–16 National Family Health Survey (NFHS) of India, the 2012–13 District-Level Household and Facility Survey (DLHS) of India, and the Election Commission of India (ECI).

We find that exposure to female leaders increases female constituents’ reported experience of physical spousal abuse. Our estimates imply that a one standard deviation (s.d.) increase in the fraction of seats held by women in a district leads to a 0.07 s.d. increase in reported physical spousal violence experienced by rural women, with no evidence of a significant impact in urban areas. We find no evidence of significant changes in attitudes toward violence, women’s labor market outcomes, or partner characteristics due to exposure to female leaders, ruling out these changes as potential channels underlying our IPV results.

Instead, we show that a plausible channel underlying the increase in IPV against women is an increase in women’s modern contraceptive use, that is potentially driven by an increase in the local availability of family planning and reproductive health services, due to exposure to female leaders. The village-level data on public health infrastructure show that districts with a higher share of seats held by female politicians experience improvements in access to health facilities, the implementation of programs which target maternal and adolescent girls’ health, and the availability of female health workers. Moreover, rural female constituents in districts with female leaders increase their use of modern methods of contraception, and experience an increase in birth spacing, with no evidence of a significant impact in urban areas. Although higher takeup of contraceptive methods can benefit women in various ways (Joshi and Schultz 2013; Miller and Babiarz 2016), as Ashraf et al. (2014) show, it can also lead to spousal conflict among couples with discordant fertility preferences. In the Indian context, married couples exhibit significant misalignment in their self-reported ideal number of sons. When the husband desires more sons than the wife, the wife’s take-up of modern contraception might lead to backlash from dissatisfied and aggrieved husbands. Indeed, we find that our IPV results are driven by couples in which the husband has a stronger son preference than the wife.

We note upfront that using self-reported data on IPV has potential disadvantages. If female leaders increase female constituents’ sense of self-worth, they might start recognizing violent acts by their spouses as IPV and might be more likely to report IPV. While we cannot entirely rule out the presence of such reporting bias, we find no evi2
Youth in the metropolitan and rural area are more likely than youth in other urban areas to leave school. In rural areas this may be explained by poverty, distance to schools, and need for supplying farm labor, while in the metropolitan area security (discussed in Section 6) and increased labor market opportunities are likely explanations for this finding.

Females are statistically significantly more likely than their male peers to drop out of school. Possible explanations are that female youth may have to help take care of their siblings, perform housework, etc. (see Section 5). Likewise, marriage has an even larger positive impact on the probability of dropping out of school. Marriage may mean having children, which negatively affect females’ school attendance, and for males marriage may mean increased responsibility and a new position as breadwinner, leaving little time or money for attending school.

Table 4.4: Probability of Youth being Out of School, 2001<sup>20</sup>

|Dropout|Coefficient|Std.Err.<sup>21</sup>|t|
|---|---|---|---|
|Age|0.664|0.127|5.21|
|Age squared|-0.011|0.003|-3.22|
|Female*|0.142|0.044|3.20|
|Family size|-0.028|0.008|-3.46|
|Metropolitan*|0.192|0.069|2.76|
|Rural*|0.090|0.051|1.77|
|Primary education*|-1.690|0.090|-18.77|
|Secondary education*|-2.487|0.100|-24.79|
|Married*|1.285|0.112|11.47|
|Head–primary education*|-0.153|0.051|-2.98|
|Head–secondary education*|-0.208|0.068|-3.07|
|Head–tertiary education*|-0.410|0.165|-2.48|
|Catholic*|0.171|0.058|2.95|
|Voodoo*|0.276|0.161|1.72|
|Other religions*|-0.083|0.067|-1.24|
|Constant|-7.024|1.213|-5.79|

_Note:_ No. observations: 6078. * is a discrete dummy variable; t is the test of the underlying coefficient being equal to 0. Household heads excluded. Variables left out: Urban, no education, head with no education, Baptist.

_Source:_ Own calculations based on ECVH 2001.

Parents’ completed level of education serves as a protective factor for children and youth attending school. The education of the household head is used as an indicator for parents’ education (see Section 5). Table 4.4 shows a statistically significant negative effect of the heads’ level of completed education on the probability of their children not being in school.<sup>22</sup> The more education attained by the head of household, the less likely it

> 20 Family size squared and migration status were included as explanatory variables in early regressions, but came out statistically insignificantly different from zero.

> 21 Standard errors are adjusted for the clustering process in the sample procedure from the EVCH survey. 22 This is in line with other studies (e.g., Saraví 2002).

14
# **5 Annex: Detailed RPSR Indices**

- A. Generation and transmission planning index for Uganda and comparators, 2015

||**Kenya**|**Senegal **|**Tanzania **|**Uganda**|**International**<br>**Benchmark**|
|---|---|---|---|---|---|
|**Generation Planning**|86%|43%|43%|43%|56%|
|Countryhas ageneration masterplan|||||94%|
|Countryhas an overall energy plan|||||65%|
|Competent entityis responsible forproducingtheplan|||||88%|
|Inter-governmental committee oversees theplanningunit|||||29%|
|Powergeneration systemplan is mandatory|||||19%|
|Plan leads to timelyinitiation ofprocurement|||||38%|
|Planning process is transparent andparticipatory|||||59%|
|**Transmission Planning**|75%|100%|75%|75%|72%|
|Competent entityis responsible forproducingtheplan|||||100%|
|Explicitlylinked topowergenerationplans|||||88%|
|Plan is mandatory|||||29%|
|Planning process is transparent andparticipatory|||||71%|

- B. Generation and transmission procurement index for Uganda and comparators, 2015

||**Kenya**|**Senegal **|**Tanzania **|**Uganda**|**International**<br>**Benchmark**|
|---|---|---|---|---|---|
|**Procurement of Generation**|100%|50%|100%|95%|85%|
|There is a framework forprocurement|||||82%|
|Country allows International competetive bidding or public<br>auctions forprocurement|||||94%|
|Types ofprocurement methods allowed||||||
|_Unsolicited bids_|||||29%|
|_Direct negotiation_|||||47%|
|_International competitive tendering_|||||88%|
|_Public auctions_|||||41%|
|_Stand-alone capacity market_|||||0%|
|Auction design score|NAP|NAP|NAP|0.86|80%|
|_Country usespublic auctionsforprocurement_|||||41%|
|Clear and comprehensive established rules|_NAP_|_NAP_|_NAP_||100%|
|Crediblepenalties for violatingthe rules|_NAP_|_NAP_|_NAP_||86%|
|Guarantees andpenalties to ensure timelycompletion|_NAP_|_NAP_|_NAP_||86%|
|Standard,non-negotiable contracts|_NAP_|_NAP_|_NAP_||86%|
|Stapled financingterms or risk mitigation instruments|_NAP_|_NAP_|_NAP_||86%|
|No concerns regardingthe transparencyand fairness of the auction|_NAP_|_NAP_|_NAP_||14%|
|Efforts to inform and attract bidders to the auction|_NAP_|_NAP_|_NAP_||100%|
|**Transmission Procurement**|67%|42%|92%|92%|64%|
|Framework forprocurement of new transmission lines|||||59%|
|Methods used toprocure new transmission-|||||69%|
|_Competitive tender_|||||65%|
|_Direct negotiation_|||||24%|
|_Allprojects are awarded to the incumbent transmission company_|||||47%|

Satisfactory result Unsatrifactory result NAP Not applicable NAV Not available 47
percent in the 2007 CHIP survey (Li 2012). Several other policies were introduced to benefit farmers during the first decade of the new century. These included compensation policies to return farmland to forest, a farm support program involving agricultural subsidies, and rural infrastructure development. From 2004 to 2011, the growth rate of central government funds to support agriculture grew by nearly 30 percent per annum (Li 2012).

In 2002, the poorest quintile of rural households spent a quarter of their income on education (Knight et al. 2009:317). An important redistributive policy with shortand long-term consequences was the abolition of all school fees in compulsory (nine-year) education. This policy was introduced in poor rural areas in 2005 and was extended to all rural areas in 2007.

The minimum income guarantee ( _dibao_ ) system became important in rural China only after 2005, reaching 52 million people in 2010. The _dibao_ system had been introduced earlier in the cities; by 2010, it covered 23 million urban people (Li 2012). _Dibao_ helped the unemployed, those in ill health, and the elderly. However, because of poor coverage and low benefit levels, it had a limited effect on urban poverty (Ravallion 2012) and even less of an effect on income inequality. Another form of intervention in urban China that was intended to be redistributive was the introduction and extension of minimum wages in many cities. Real minimum wages have risen rapidly in recent years, reflecting the central government’s directions and incentives.

20
Brookshire, D. S., Chang, S. E., Cochrane, H., Olson, R., Rose, A., Steenson, J., 1997. Direct and indirect economic losses for earthquake damage. Earthquake Spectra 13, 683701.

Cavallo, E., and Noy, I., 2009. The economics of natural disasters - a survey. Inter-American Development Bank Working Paper #124.

Cho, S., P. Gordon, J.E. Moore, H.W. Richardson, M. Shinozuka, and S. Chang. 2001. Integrating transportation network and regional economic models to estimate the costs of a large urban earthquake. Journal of Regional Science, 41:39-65.

Cochrane, H., 1974. Social science Perspectives on the Coming San Francisco Earthquake: Economic Impact, Prediction, and Reconstruction. Natural Hazard Research Working Paper No. 25. Boulder, CO: Institute of Behavioral Science, University of Colorado.

Cochrane, H., 2004. Economic Loss: Myth and Measurement, Disaster Prevention and Management 13, 290-296.

Cole, S., 1998. Decision support for calamity preparedness: socioeconomic and interregional impact, in M. Shinozuka et al. [Eds.], Engineering and Socioeconomic Impacts of Earthquakes: An Analysis of Electricity Lifeline Disruptions in the New Madrid Area. Buffalo, NY: MCEER.

Coluzzi, B., M. Ghil, S. Hallegatte, and G. Weisbuch, 2010. Boolean delay equations on networks: an application to economic damage propagation, Quantitative Finance Papers, arXiv.org Delli Gatti, D., Di Guilmi, C., Gaffeo, E., Giulioni, G., Gallegati, M., Palestrini, A., 2005. A new approach to business fluctuations: heterogeneous interacting agents, scaling laws and financial fragility. Journal of Economic Behavior and Organization 56 (4), 489-512.

Gordon, P., H. Richardson, and B. Davis, 1998. Transport-related impacts of the Northridge 43
_World Development Indicators_ and span the years 1975 to 2005.<sup>3</sup> Our sample contains all sub-Saharan African countries for which PPP GDP data exist. There are no GDP per capita PPP data for Liberia, San Tomé and Principe, and Somalia, and they are excluded from the analysis.<sup>4</sup> The un-weighted mean GDP per capita between 1975 and 2005 for the 44 countries in our sample was $2,306. Mean GDP per capita using GDP weighted data was $1,702.<sup>5</sup> Table A1 in the appendix presents descriptive statistics for income and growth at the country level.

Figure 1 presents the time paths of un-weighted and GDP weighted per capita income growth rates.<sup>6</sup> Although the trajectories of the un-weighted and weighted series appear similar, their means and variances are significantly different. The region’s unweighted average growth rate was 0.71 percent and its standard deviation (SD) was 6.32 per cent. The mean and SD of the weighted data are –0.17 percent and 1.7 percent, respectively, reflecting the fact that Africa’s bigger economies grew more slowly than its smaller ones. Between 1975 and 2005, South Africa, which represents on average 42

**Figure 1: GDP per capita growth 1975-2005**

<!-- Start of picture text --> Figure 1 a: Un weighed d ata<br>8<br>4<br>0<br>8<br>-4<br>4<br>0<br>-4<br>-8<br>1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>Growth Trend Cycle<br><!-- End of picture text -->

> 3 !975 is the first year available for this indicator.

> 4 We, thus, have a panel of data with 44 countries and 31 periods.  Our sample accounted for 98.4 percent of population and 99 percent of regional GDP in 2005.  Although Equatorial Guinea is in our sample, we removed the country from charts, econometrics, and aggregate descriptive statistics because its extremely high growth rates in recent years distort many of the results.

> 5 Although there are differences between GDP per capita at PPP and non-PPP, those differences are confined to levels and do not affect growth trajectories.  PPP and non-PPP growth data share similar statistical properties.  See Arbache and Page (2008) for a fuller discussion.

> 6 We employ the Hodrick-Prescott filter in Figure 1 to smooth the estimate of the long term trend component of the GDP growth series.

4
(2013), we construct synthetic panel data from the two rounds of cross sections ESPS 1 and 2. In the absence of true panel household survey data, these synthetic panel data allow us to profile the population that remained in poverty or moved out of or into poverty in this time period.

Our findings point to considerable movement across the poverty line in Senegal during 20062011 - both upwards and downwards. This is in contrast with the relatively stable cross sectional poverty rates. Poor households were somewhat more likely to escape poverty than non-poor households falling back into poverty, and more likely to have enjoyed stronger consumption growth. More than half of the population experienced either upward or downward mobility; in particular, more than two-thirds of the extreme (food) poor moved up one or two welfare categories. Factors such as education achievement, having a female household head, urban residence, and formal work contract are strongly correlated with poverty reduction. The correlates that help prevent households from falling into poverty include education achievement, work contract, urban residence, having a female head, migration, belonging to other ethnic groups, working in the non-agriculture sector, social capital, having no disaster, and having no disability. In particular, compared to all the other correlates, education appears to be most effective in pushing households out of poverty and keeping them from falling into poverty.

It is particularly important to note that these factors can have varying degrees of association with chronic poverty or downward mobility. Policy makers interested in designing social protection programs (e.g., cash transfers) may want to look closely at those factors that are strongly correlated with downward mobility. These may point to instances where the “social safety net” needs to be repaired or strengthened. The evidence suggests that in Senegal efforts to build up social capital, provide insurance against natural disasters, and improve community infrastructure might serve to diminish the vulnerability of the population to falling into poverty.

29