rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 12:  Distribution of Congressmen by positions aspired for in 2010 elections**

|**Classification of Congressman**||**Position Aspired**<br>|**for in 2010**<br>||
|---|---|---|---|---|
||**LCE Post**<br>_(Governor or_<br>_Mayor)_|**Local Legislative**<br>**Post**<br>_(Vice Governor_<br>_or Vice Mayor)_|**Senate**|**President/**<br>**Vice President**|
|HOR Members|||||
|a. First Term|9|1|1|-|
|b. Second Term|2|1|2|-|
|c. Third Term|30|6|3|-|
|**HOR Members Total**|**41**|**8**|**6**|**-**|
|Senate Members|||||
|a. Mid-Term (elected 2007-13|-|-|-|4|
|b. End Term (elected 2004-2010)|1|-|4|4|
|**Senate Members Total**|**1**|**-**|**-**|**8**|

Source: Congress The analysis here has focused on the presumed career-related incentives of national legislators as a basis for imputing their likely stances on legislative proposals related to LGUs. But obviously there is no mechanistic correlation between these career-related variables and the actual voting behavior of the legislators. For example, 13 of the 62 co-authors of the HB No. 3708 have never been an LCE, have no relative running LGUs and did not run for local elections in 2010. Some individual legislators join in the co-authorship of a bill out of personal interest/conviction or political convenience and it is impossible to predict a precise number of such legislators _ex ante_ .<sup>29</sup> Likewise, legislators who are found to have career incentives related to LGUs cannot always be counted on to support a bill to strengthen LGUs’ fiscal capacities because in some specific circumstances doing so would amount to strengthening the hands of incumbents who happen to be their political rivals.

Interviews with selected legislators and others close to the discussion (e.g., representatives of the League of Provinces of the Philippines, LPP) have revealed a range of political calculations the stakeholders make with respect to the relatively simple proposal of increasing the LGUs’ shares in the national taxes. Interviewees frequently talked about a tactic of getting the increase approved legislatively in exchange for some politically desirable support LGUs could offer, such as support for the President’s constitutional change initiative. The notion that national politicians, either the president or legislators, would cater to LGUs’ demand rests on the premise that local politicians hold key to assets or resources that are valuable to national politicians. Typically, it is argued that national politicians are dependent on local politicians for mobilizing votes because of the absence of a well-organized political party that can maintain vote-getting machinery. Therefore, to the extent a particular local politician (elected or not) can mobilize a large number of voters reliably, his/her ability to extract concessions

> 29 For example, Cong. Rufus Rodriguez (Cagayan de Oro, 2<sup>nd</sup> Dist.) attributes his decision to support the bill to his learning about the plight of LGUs from his legal scholarship. Rodriguez, apart from authoring four bills related to IRA in the 14<sup>th</sup> Congress, has also authored a number of books related to local governance.

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17 This view could be—and sometimes is—challenged. In some cases there may well be some discretion (at least at the margin) over health expenditures. However, in most cases, it seems more reasonable to treat health spending as non-discretionary and to view it as not contributing to household welfare. This would point to _not_ including it with other items of household spending in consumption aggregates in studies of household living standards. This is the conclusion that Deaton and Zaidi<sup>24</sup> reach based in part on the low income-elasticities of health spending they find in six out of the seven developing countries they study. Burtless and Siegel<sup>25</sup> also argue for this approach in their discussion of proposals to take explicit account of health care spending in computing US poverty rates.

While treating health expenditures as involuntary seems reasonable, the implied practice of not including out-of-pocket spending in consumption aggregates when measuring poverty is often not followed. The World Bank’s official dollar-a-day poverty figures, for example, are actually based on measures of household consumption that include out-of-pocket spending on medical care. This makes for lower poverty rates than would be the case if out-of-pocket spending on medical care were treated as involuntary and excluded from the consumption aggregate.<sup>23</sup>

# **Asset sales, dissaving and borrowing**

The second assumption underpinning the approaches above is that the household’s non-medical expenditure in the period under consideration would have increased by an amount equal to its out-of-pocket expenditures on medical care had it not been forced to incur the out-of-pocket spending. The assumption, in other words, is that
- Saxena, P.C., Kulczycki, A. and Jurdi, R. (2004). Nuptiality transition and marriage squeeze in lebanon: Consequences of sixteen years of civil war. _Journal of Comparative Family Studies_ 35(2):241–258.

- Sekhri, S. and Debnath, S. (2014). Intergenerational Consequences of Early Age Marriages of Girls: Effect on Childrens Human Capital. _The Journal of Development Studies_ 50(12):1670–1686.

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peer comparisons and adaptation to circumstances as an integral part of the “happiness function”.

In addition, there is no systematic evaluation of historical life events and current emotional state in reported life-satisfaction, except in some laboratory experiments. The importance of retrospective events on current mental state has been proposed by psychologists and economists (Easterlin, 2003; Lucas, et al., 2004; Kahneman and Krueger, 2005). Some of the recent work on life events includes Wu (2001) and Lucas (2005) on adaptation to marriage and divorce, Oswald and Powdthavee (2005) on adaptation to illness and Lucas et al. (2004) on adaptation to unemployment. Di Tella et al. (2005) find that a large share of the effect of income increases on happiness vanishes. They note that the effect after four years is only about 42% of the effect after one year. In another interesting study, Burchardt (2005) finds evidence of greater adaptation to rises in income than to falls in income. In one of the earlier works, Inglehart and Rabier (1986) documented that aspirations adapt to circumstance, such that, in the long run, stable characteristics do not affect well-being. There is lack of consensus among researchers whether the strength of adaptability depends on the nature of an event (say job loss versus marriage) or time (nothing matters in the long run)<sup>1</sup> .

There is evidence of third party evaluation on respondents’ life satisfaction to validate their answers (Diener and Lucas, 1999). Possible choices could include family members, friends or the interviewer. As documented by Kahneman and Krueger (2006), measures of temperament and personality typically account for much more of the variance of reported life satisfaction than do life circumstances, but empirical research on this issue is very scant.

In this paper we look at life-satisfaction outcomes within transition countries using Life in Transition Survey (LiTs) data for 27 transition countries<sup>2</sup> . We measure comparison

> 1 See Kimball and Willis (2006) for an excellent review of the psychology of adaptation

> 2 Countries included from European region: Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, and Slovenia; Balkan region: Albania, Bosnia and Herzegovina, Croatia, Macedonia, Montenegro, and Serbia; low-income CIS region: Armenia, Azerbaijan, Georgia,

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15 and could not reject its presence. The results of the standard F-test are therefore biased, and we used the procedure suggested by Honda and Ohtani (1986) and Honda (1988). The resulting Generalized Covariance (Chow) test is based on a Chi-square statistic (Table 5, bottom panel) which rejects the null hypothesis. This indicates that the data of public and private enterprises fit different models, as the basic statistics reported in Table 4 suggest. Our formal statistical analysis therefore suggests that the productivity response of public enterprises after the reforms has been significantly different from that of private firms. A plausible interpretation of this result, following the reasoning reported above, is that stateowned enterprises lag significantly behind in the adjustment process compared to private firms. The successful case-studies reported by Pinto et al. (1993) seem therefore to provide a partial picture of the productive performance of public enterprises, as recognized by the same authors and suggested by the data collected by Estrin et al. (1994).
model includes the first, second, and 12th autoregressive lags, as well as a quadratic time trend. Adding lags of the dependent variable also accounts for serial correlation.

Equation (3) is estimated for regular cars and all private vehicle registrations, controlling for seasonal variation (table 6, panel (a), see columns (1) and (2)). In both specifications, the PYP effects for the moderate phase are positive but statistically insignificant. Interestingly, the PYP estimates for the drastic phase, besides being positive, are statistically different from zero at the 5 percent and 10 percent significance levels for regular and all private vehicle registrations models, respectively. The size of the long-run estimates for this phase is around 10 percent, though this estimate is less precise for the specification that includes all private vehicles. Relative to the moderate restriction, the long-run PYP effect was positive and statistically significant at the 10 percent level in both specifications. This provides mild evidence of an increase in vehicle registrations during the drastic restriction, which is also consistent with no decrease in CO and the slight indication of increased CO for the morning peak in the same phase. The estimates are in between the vehicle stock increases reported by Davis (2008) for HNC in Mexico City (19 percent) and Gallego, Montero, and Salas (2013) for TS in Santiago (8 percent). As a robustness check, the effect of the program on load vehicle (truck) registrations is tested using a similar model. These vehicles were not restricted by the program, and one might expect that PYP did not affect load vehicle registrations (see column (3) in panel (a) of table 6). Indeed, neither the moderate nor the drastic phase of the program influenced the number of truck registrations.

Vehicle ownership does not seem to have been affected by the moderate ban. One potential explanation for this is that households may not have been inclined to own additional vehicles because they could still drive without restrictions during off-peak hours. During the moderate phase, households did not have prior experience with the 20
percent of Cambodia’s total exports in the early 1990s, but by 2003 it constituted 76.4 percent. Sri Lanka, already a seasoned apparel exporter in the mid-1990s, experienced a healthy but more moderate growth than Cambodia, with exports rising from $1.7 billion in 1995 to $3.0 billion in 2004 (figure 1). However, in the late 1990s export growth decelerated, and exports even declined in 2001 and 2002.<sup>7</sup> <!-- Start of picture text --> Figure 1 Global Apparel Exports from Cambodia and Sri Lanka, Volume and<br>Annual Growth<br>115 4,500<br>4,000<br>95<br>3,500<br>75 3,000<br>2,500<br>55<br>2,000<br>35 1,500<br>1,000<br>15<br>500<br>-5 0<br>1998 2001 2004 2005 2006 2007 2008<br>SL value Cam value SL growth Cam growth<br>growth, percent<br>total value, US$ millions<br><!-- End of picture text --> _Source:_ United Nations Commodity Trade Statistics Database (UN Comtrade). _Note:_ The growth rate reflects the change from the previous year.

Despite pessimistic expectations for the apparel sectors post-MFA, both countries continued increasing apparel exports, though growth slowed somewhat. Sri Lanka expected that exports would decrease by half and that 40 percent of firms would close in 2005 (Kelegama and Epaarachchi 2002). However, Sri Lankan apparel exports grew 6 percent annually on average, and their value increased by approximately $1 billion over 2005–08. In Cambodia immediately after the MFA removal, total apparel exports increased to $2.7 billion in 2005 and to $4.0 billion in 2008, a rise of almost 14 percent annually.

In both countries employment in the apparel sector grew under the MFA. The post-MFA dynamics were quite different: while in Cambodia the number of workers in the industry continued to grow because of industry expansion, in Sri Lanka’s sector employment declined because of industry consolidation. Given that women represent the major share of employment in apparel manufacturing, the general employment trends are directly applied to women. Employment in Cambodia’s apparel sector mushroomed from fewer than 19,000 workers in 1995 to approximately 270,000 in 2004 (Staritz and Frederick 2011a). Growth continued after the MFA phase-out, with operating employment reaching 353,017 workers in 2007 (see figure 2).

7This decline in Sri Lanka’s exports was related to lower demand in developed countries but more importantly to the bomb attacks at the Colombo International Airport in July 2001 that triggered the imposition of war-risk insurance charges (Kelegama and Wijayasiri 2004). The reduction in orders and escalating insurance costs hit the industry severely, and as a result several small- and medium-sized enterprises closed in the early 2000s.

4
|Table VII(a)<br>Effect of Political Affiliation on Natio|nal Highwaysa|
|---|---|
|1 yr before Elections* Affiliated|-40.36|
||[-1.30]|
|2 yrs before Elections*Affiliated|-79.92|
||[-2.60]|
|3 yrs before Elections*Affiliated|-33.95<br>[-1.15]|
|4 yrs before Elections*Affiliated|-47.88|
||[-1.41]|
|1 yr before Elections* Non-affil.|-5.26<br>[-0.15]|
|2 yrs before Elections* Non-affil.|-29.93<br>[-0.81]|
|3 yrs before Elections* Non-affil.|-14.58|
||[-0.40]|
|4 yrs before Elections* Non-affil.|-33.63|
||[-1.00]|
|Political Affiliationb|-28.90|
||[-0.90]|
|Number of Observations|318|
|R-sq|0.1715|

a. Regression includes state and year effects.

b. Indicator equals 1 if the incumbent is affiliated with the centre.

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household members’ individual incomes is divided by the number of household members to give the income per capita. We use regional poverty lines taking a R$ 80 per capita poverty line (in current 2001 prices) for urban Rio de Janeiro as a basis and adjusting for regional price differences following Paes de Barros (2004). The poverty lines are reported in appendix 7.4.

# _3.2.4 Shortcomings_

The household income generation process is of course much more complex than the proposed micro-simulation model suggests. Many structural features of the Brazilian labor market, such as a high degree of informality in wage-employment and the important role of self-employment, are only accounted for rudimentarily. Additionally, the data used to estimate the mover-stayer model may capture long-term transitions from agricultural into non-agricultural employment, but also some temporary job shift, for example due to seasonality. Doubts have emerged regarding the reliability of the information drawn from the PNAD, in particular on rural, informal sector, and capital income (World Bank 2003).

Finally, it may seem natural to assume that the sectoral movements are somehow related to geographical migration from rural to urban areas, either within or even between the Brazilian regions.<sup>28</sup> Traditionally, migration has acted as an important adjustment mechanism in Brazil. Nearly 40 percent of all Brazilians have migrated at one point in their lives (Fiess and Verner 2003). However, the data does not allow to link intersectoral movements to geographical migration. First, migration-related questions of the PNAD do not cover rural-urban migration. The available information is on whether an individual has moved between municipalities and/or between federal states. Second, using the information on migration in combination with the employment history from the PNAD 2001 suggests that only a minor share, of about 12 percent of those leaving the agriculture, actually migrates to another municipality. Approximately half of these migrants move to another federal state. In light of the importance of migration as an adjustment mechanism and the fact that the decline in agricultural employment is accompanied by a reduction in the rural population of equal magnitude, we think that these figures reflect data deficiencies rather than the Brazilian reality. It is quite likely that migrants are underrepresented in the PNAD. Therefore, we had to ignore the issue of geographical migration here due to the low share of “geographical” migrants among the

> 28 This would of course add to the list of arguments why the labor market for the unskilled should be considered segmented.

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# **ANNEX Figure 4 ‐ SSA Imports from Developing Countries**

<!-- Start of picture text --> Total imports per capita<br>(in current US$)<br>400.00<br>350.00<br>300.00<br>250.00<br>200.00<br>150.00<br>100.00<br>50.00<br>0.00<br>China Gulf countries India Other<br>2000 2007<br>Total imports in % of GDP<br>2.50<br>2.00<br>1.50<br>1.00<br>0.50<br>0.00<br>China Gulf countries India Other<br>2000 2007<br><!-- End of picture text --> Source: Author’s calculations.

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rapidly. These developments in the post 1994 era may have affected their fertility. The “middle” group consists of women who were of primary and secondary school age or slightly older during the genocide. For many women in this age-group, schooling was brutally interrupted by the 1994 events. Although schools in Rwanda reopened in late 1994, many parents and children did not want to go back to schools, which had been main scenes of mass slaughter during the genocide. For many of those women education was prematurely and permanently cut short, which may have influenced future economic and fertility outcomes. Finally, the upper age-group consists of women who grew up in the 1970s and 1980s and had completed their education before the genocide.

The first thing to note in Table A1 is that although fertility dropped in each age-group between 2005 and 2010, the drop was relatively stronger for the younger groups: Age-specific cumulative fertility decreased by 16 percent for the youngest age-group, 14 percent for the middle-age group and 9 percent for the oldest age-group. The increase in average educational attainment between 2005 and 2010 is particularly important for the young women and for the older women. In 2005, the average number of years of education completed amounted to 3.9 for young women and 3.1 for older women. In 2010 this had increased to 5.3 and 4.1, respectively. Young women are also staying in school longer: Over 37 percent of young women were still in school during the 2010 survey, up from 20.6 percent during the 2005 survey. The effect of the genocide is reflected in the evolution of education outcomes of the middle age-group: While the average educational attainment of women between the ages of 25 and 34 amounted to 4.7 in 2005 it dropped to 4.5 in 2010. Women who were in this age-bracket during the 2010 survey were born between 1976 and 1985 and had started schooling before the genocide cut it short: Women from this cohort were more likely to have ever been enrolled in school compared to women born five years earlier (witnessed by the lower percentage of women who never went to school in Table A1), but were less likely to have completed primary school and completed on average 0.2 fewer years of education.

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