rafmacalaba/datause-encoder-data huggingface dataset:
A common motivation of the proposals discussed above is that the decisions made by countries in setting their national poverty lines should be viewed as the guiding social assessments of how needs change as countries become richer.<sup>9</sup> To this end, parameterizations of the global relative poverty line are often informed by assessing the relationship between national poverty lines and mean expenditure or consumption levels for a sample of countries. The Atkinson and Bourguignon line is informed by a bivariate fit based on 33 estimated national poverty lines (described in Ravallion, Datt and van de Walle, 1991), while the Ravallion and Chen line is supported by a sample of 74 national poverty lines (described in Ravallion, Chen and Sangraula, hereafter referred to as RCS, 2009).

Gentilini and Sumner (2012) take the idea of viewing national poverty lines as a guide for assessing global poverty a step further and propose a global measure of poverty that is simply the sum of all poor people in the world as identified by their national poverty line. There is an elegance to the simplicity of this proposal and since the global count of the poor is completely determined by national poverty lines, it fully respects each country’s social assessment of poverty. But, implicit in this approach is an underlying international poverty line (or schedule) that is neither fixed in value across countries, as in the international poverty line of $1.90, nor is the implicit line strictly increasing in economic development. In fact, there are many countries at the same level of economic development that have vastly different assessments of basic needs, and similarly there are many cases where richer countries have lower poverty lines relative to poorer countries.<sup>10</sup> An awkward implication of this approach is that the global aggregation of

> 9 This idea of viewing national poverty lines as reflecting social assessments of minimum needs has been a motivating argument for international poverty lines for many years. World Bank (1990) and RDV (1991) interpreted national poverty lines from some of the poorest countries in the world as reflecting absolute minimum needs and used them to establish the “dollar-a-day” international poverty line.

> 10 Figures 2 and 3 both provide evidence of this.

8
Routley, Laura, and David Hulme (2013). _Donors, Development Agencies and the Use of Political Economy Analysis: Getting to Grips with the Politics of Development_ . Effective States and Inclusive Development Working Paper Series No. 19. (Manchester: Global Development Institute).

Royal Government of Cambodia (2007), _Aid Effectiveness Report 2007_ (Phnom Penh: Cambodia Rehabilition and - Development Board of the Council for the Development of Cambodia) available online at <u>http://www.cdc crdb.gov.kh/cdc/aid_management/AER-Report-2007-FINAL.pdf, accessed 23 April 2017.</u> Stuart Fox, Martin and Bunheang Un (1998), _The Murderous Revolution_ (Bangkok: Orchid).

Taliercio, Robert (2009), “Unlocking Capacity and Revisiting Political Will: A Review of Cambodia’s Public Financial Management Reforms, 2002-2007,” _International Public Management Review_ 10.1: 90-118.

Tushman, Michael and Charles O’Reilly (1996), “Ambidextrous Organisations: Managing Evolutionary and Revolutionary Change,” _California Management Review_ 38.4: 8-30.

Transparency International (2017), _Corruptions Perceptions Index 2016,_ online at <u>http://www.transparency.org/news/feature/corruption_perceptions_index_2016, accessed 23 April 2017.</u> Un Kheang (2005), “Patronage Politics and Hybrid Democracy: Political Change in Cambodia 1993-2003,” _Asian Perspective_ 29.2: 203-230.

Willemyns, Alex and Kuch Naren (2015), “For Reform Agenda, CPP Takes Cues from CNRP,” _The Cambodia Daily,_ 16 January, online at https://www.cambodiadaily.com/news/for-reform-agenda-cpp-takes-cues-from-cnrp- <u>105049/, accessed 26 April 2017.</u> World Bank (2015a), Managing Change in PFM System Reforms, a Guide for Practitioners (World Bank Group: Washington DC).

World Bank (2015b), Cambodia Public Expenditure Framework Assessment (PEFA) Review, (World Bank Group: Washington DC).

World Bank Indonesia (2015c), A presentation of SPAN – Lessons Learned from an eleven years of FMIS Implementation in Indonesia, Public Sector Management Team, Governance Global Practice.

World Bank (2016) _Government Financial Management and Revenue Administration Project,_ Intensive Learning Implementation Completion and Results Report, (Jakarta, World Bank), online at <u>http://documents.worldbank.org/curated/en/783111467624296787/pdf/ICR3795-P085133-Box396252B-PUBLICdisclosed-6-29-16.pdf, accessed 26 April 2017.</u> 38
that access to credit remained to be the key constraints for adopting self-financing strategy. Column (6) employs the multinomial logit regression which confirms the same narrative that private resources available in the local economy helped recipients of poor aid boats undo it in the later stage.

# **6.      Post-tsunami evolution of income inequality**

Finally, I examine how the two types of quality shocks affected income distribution over time. In the rural economy without formal social insurance system, the loss of jobs or production capacity could bring fishermen into poverty. Therefore, if they could not cope with the quality shock in each stage through two measures (labor adjustment and self-financing), I would expect to see a significant income disparity between the treated group (those affected by the quality shock) and the control group (those not affected).

Figure 5 shows the change in per capital income distribution (in real term) from pre-tsunami to 2009. The income inequality was determined by the initial wealth endowments before the tsunami, but was equalized as almost all financial and physical assets were wiped out by the tsunami ( _leveling effect_ ). However, income inequality has diverged over time, especially with increasing number of poor households at the lower tail of income distribution in 2009. This suggests that both aggregate and idiosyncratic quality shocks created a _new form of inequality_ depending on the type of aid and jobs household obtained after the tsunami. The following first-differenced (FD) model is used to estimate the effect of the quality shocks on income growth.

where Δln(yivk) is real income growth for household _i_ , in village _v_ and kecamatan _k_ . It is the log difference of real per capita income: ∆ln(yivk) = ln ~~�~~ nI0909,ivk,ivk � −ln �nI0707,ivk,ivk ~~�~~ where It is weekly earning (in real term, deflated by the consumer price index in Aceh province from BPS ( _Badan Pusat Statistik_ ) website) and nt is household size in year _t_ . Right hand side includes a dummy for fishing 15
# **V. Rationalizing Momentum Trading: Return Autocorrelation?**

In an environment with positively autocorrelated returns, momentum trading is a natural response. The previous section presented evidence of positive LOM and, at least during non-crisis periods, positive LIM. This raises the question of whether returns within Latin America exhibit positive autocorrelation. One common way to test for return autocorrelation is using variance ratios. If returns follow a random walk, then return variance is a linear function of horizon length. That is, the variance of returns over **k** periods is **k** times the variance of returns over one-period. If instead returns are positively autocorrelated, the variance of k-period returns is larger than the sum of one-period returns-variances grow faster than linearly. Thus, variance ratios larger than one are consistent with rational positive momentum trading. Alternatively, when returns are negatively autocorrelated, the variance of k-period returns is smaller than **k** times the variance of one-period returns. Variance ratios smaller than one would call for negative momentum (or contrarian) trading.

Table **6** reports the values of the variance-ratio test statistic at different horizons, together with p-values, for seven Latin American countries. For comparison we also provide results for the **U.S.** stock market.1<sup>7</sup> Interestingly, stock returns in several Latin American markets are **highly** persistent (variance-ratio statistics larger than one), even at three and four-year horizons. In contrast, **U.S.** returns show no persistence at any horizon. Though certainly not proof that the positive momentum trading we find in Latin America is rational-after all, this persistence in returns is at the index level-these results do point to the possibility of rationalizing our momentum results, at least for some countries (e.g., Mexico, Chile, Colombia, and Venezuela).

It is important to note, however, that while positive autocorrelation is necessary for rationalizing positive LIM, it is certainly not necessary for rationalizing positive LOM. As noted in Section **11.1,** returns and trades may be truly contemporaneous if order flow itself is driving prices. This is possible where fund transactions are "large" relative to liquidity

> **17** See Campbell, Lo, and MacKinlay **(1997)** for the asymptotic distribution of the variance-ratio test.

**19**
## **I. Introduction**

The relationship between income inequality and economic growth has received significant attention in the empirical literature over the past few years. The results of most of the studies focusing on the impact of growth on inequality<sup>1</sup> suggest that there is no general tendency for inequality to move in any particular direction with growth.<sup>2</sup> Growth would therefore benefit everybody in society in a similar fashion.

This note adds to the existing literature by analyzing the stability of this relationship over time. Using data from the Dollar and Kraay (2002) database (DKD) the note concludes that the growth inequality relationship is far from stable. In particular, during the 1990s the data show a positive correlation that is not only statistically significant but also economically: a 1 percent growth rate would be associated with an increase in the gini coefficient of between .3 to .5 percent (i.e. growth has become less pro-poor in recent times). This finding, which is hidden both when one imposes a common slope over time and when one uses decadal dummies that do not take into account the unbalanced nature of the panel, survives a number of robustness checks including: (i) using alternative econometric specifications to isolate the decadal shifts; (ii) using estimation techniques that are robust to the presence of outliers; (iii) controlling for the potential impact of changes in the composition of the sample over time due to the unbalanced nature of the panel; and (iv) considering the possibility of fixed effects in the data. The rest of the note is organized as follows. Section II describes the basic empirical strategy. Section III presents the empirical results. Section IV explores how this structural change has affected the rate of poverty reduction. Finally, Section V closes.

# **II. Data and methods**

Our empirical strategy relies on the estimation of models based on variations of the following basic specification: where _g_ is the log of the gini coefficient, _y_ is the log of per capita income, Δ _T_ is the _T_ -th difference operator so that Δ _T g it_ = _g it_ − _g it_ − _T_ , and _Tit_ is the length of the spell for country _i_ and time _t_ , which as indicated by the subscript _it_ , will vary with the spell. _u_ is an error term.

Panel (a) of figure 1 plots the scatter of changes in the gini coefficient (in logs) against growth for the 1970-2000 period. The inequality data are from the DKD and the growth data is derived from the PWT6.1. Inspection of this figure suggests no apparent relationship between growth and changes in inequality. This is confirmed by column (1) of table 1 which reports the results of estimating equation (1) for the 1970-2000 period.

> 1  There is different strand on the literature that has focused on the impact of inequality on growth. Among others, see Forbes (2000) and Banerjee and Duflo (2003).

> 2  See among others Deininger and Squire (1996), Chen and Ravallion (1997) and Dollar and Kraay (2002).

2
Our results indicate the prevalence of autonomy over the rest of the variables, with tools as the index’s component with the lowest score. With the exception of Chile and Colombia, countries that have chosen a particular regulatory design for their electricity agencies, the rest of the countries in the region have opted for a model that emphasizes its independence from political authorities over other aspects of its functioning. LAC countries have also stressed agencies’ accountability before the Executive, the Legislative and the Judiciary and, to a less extent, the inclusion of mechanisms and procedures to guarantee their transparent management (transparency) and the implementation of their decisions (tools).

**Figure 5: Ranking of Electricity Regulatory Governance Variables**

<!-- Start of picture text --> Electricity Regulatory Governance in LAC<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>Autonomy Accountability Transparency Tools<br>Variable<br>Formal Informal Index<br>Iindex (0-1)<br><!-- End of picture text --> _Source:_ LAC Electricity Regulatory Governance Database. The World Bank. 2007 The region’s average in the Electricity Regulatory Governance Index (ERGI) is 0.74. Among the different variables that compose the ERGI, the region achieves its best performance in autonomy (0.79), followed, respectively, by accountability (0.73), transparency (0.72), and tools (0.66). The highest score achieved in autonomy and the lower results in other aspects of agencies’ governance might reflect the tendency of LAC countries to emphasize regulatory agencies as a means to gain credibility before investors (Gilardi, 2002). While there are degrees of variation, the majority of independent regulators in the electricity sector have a board of directors appointed by the President with the authorization of the Congress, a separate status from the line ministry, and separate budgeting (although there are different levels of autonomy in the management of funds). The lowest levels of autonomy can be found in agencies in charge of both regulation and sector planning (Chile and Colombia), where the government, through the line minister and other ministers, is part of the agency’s decision-making process.

Accountability ranks as the second variable after autonomy and indicates the level of oversight of the agency by different public bodies (usually the Parliament and the Executive branch). In addition, it reflects the existence of mechanisms for internal auditing and the regulatees’ ability to appeal agencies’ decisions. The majority of agencies in the electricity sector are subject to the oversight of both the Parliament and the Executive, although they have greater accountability obligations to the latter. Another factor that contributes to the regional score in accountability is the ability to appeal decisions before the judiciary and not exclusively the government. The review of agencies’ decisions by the judiciary prevents the government from interfering in the agency’s rulemaking and implementation of those rules.

- 15 -
# **Figure 6 Monthly Share of Rural Labor Force with Off-farm Employment: Actual and Under a Business-as-Usual Counterfactual, May 2007 to April 2009** _(percent)_

# A. China

<!-- Start of picture text --> 65<br>55<br>BAU<br>45<br>Actual<br>35<br>25<br>May-07 Sep-07 Jan-08 May-08 Sep-08 Jan-09<br><!-- End of picture text -->

# B. South and North China

<!-- Start of picture text --> 65<br>60<br>South, BAU<br>55<br>50 South, actual<br>45<br>40 North, BAU<br>35<br>North, actual<br>30<br>25<br>May-07 Sep-07 Jan-08 May-08 Sep-08 Jan-09<br><!-- End of picture text --> _Source_ : Huang et al. (2011).

_Note:_ BAU = business as usual.

24
_Notes_ : All surveyed students were asked about the main difficulty to achieve their aspired level of education. Students had to pick one answer among nine possible. Less than 1 percent responded that their main difficulty was “other” and 2 percent of students responded they did not see any barriers to achieving aspired level of education (not displayed in the graph). The “Lack of parental support” data also include responses to a possible answer labelled as “Family will not allow”, which less than half a percent of students chose.

Students who have lower expectations than aspirations are more likely to perceive economic constraints as their biggest obstacle, suggesting that students may perceive economic constraints as more challenging to overcome than mental barriers. Indeed, 31 percent of students who do not expect to achieve their aspired education level cite economic constraints as their main difficulty, compared to 18 percent of those who expect to be able to achieve their aspirations (Figure 8). Because constraints that are perceived as insurmountable would likely lead to a gap between aspirations and expectations, economic constraints may seem more difficult to overcome than other barriers. Nevertheless, mental barriers can also pose obstacles that appear difficult to overcome: 30 percent of students who do not expect to achieve their aspired education level cite a lack of study habits or determination as their main obstacle and 12 percent cite other mental barriers (compared to 34 and 14 percent of students who expect to achieve their aspired education level, respectively).

Girls are more likely to cite economic constraints as their main difficulty to achieving their aspired education level (29 percent of girls compared to 20 percent of boys). This does not seem driven by a lack of parental will to invest in girls’ education: similar levels of girls and boys cite lack of parental support (financial or encouragement) as their main barrier. Instead, the result is likely linked with girl’s overall higher aspirations. As discussed in section 5.1, 86 percent of girls would like to complete a postsecondary degree, and 54 percent of girls aspire to PhD, compared to 67 and 39 percent for boys respectively. Completing a postsecondary degree, and PhDs in particular, requires more resources than lower levels of education due to a need to cover both tuition and living expenses for a longer period of time while studying. This may also be that restrictive gender norms, lack of childcare options, and low labor market participation and lower wage of women in Indonesia, make some families perceive girls’ education as less valuable and thus costly (Afkar et al. 2020).

23
World 2030 <!-- Start of picture text --> 0 0 |<br>— -10 _ -10<br>&=<br><!-- End of picture text -->

<!-- Start of picture text --> ®=] 1 © 1<br>o H a !<br>& Climate models 5 Climate models:<br>S -20 & -20<br>3 m HadGEM2-ESIPSL-CMSA-LR | 3S m HadGEIPSL-C M S5A-LR2-ES |<br><!-- End of picture text -->

<!-- Start of picture text --> MIROC-ESM-CHEM MIROC-ESM-CHEM<br>“30 = NorESM1-M “30 = NorESM1-M<br>1 ~ 1 mTM<br>; ° °<br><!-- End of picture text --> <!-- Start of picture text --> fe}c fe}Cc<br>RCP 2.6 RCP 8.5 RCP 2.6 RCP 8.5<br><!-- End of picture text -->
|Book-to-market|-0.00287|-0.00293|-0.00645***|-0.00490**|-7.355***|-7.356***|-6.145***|-7.144**|
|---|---|---|---|---|---|---|---|---|
||(0.00203)|(0.00189)|(0.00154)|(0.00186)|(1.740)|(1.866)|(1.500)|(2.257)|
|Observations|2182|2182|1859|1859|111|111|108|108|
|Adjusted R-squared|0.043|0.045|0.047|0.058|0.103|0.089|0.049|0.209|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

47
**Figure 11 Change in production and yield of cereal grains from 1993 to 2007**

|Albania|Production<br>|
|---|---|
||Yield|
|Argentina||
|Armenia||
|Azerbaijan||
|Belarus||
|Bosnia & Herzegovina||
|Bulgaria||
|Canada||
|China||
|Colombia||
|Croatia||
|Czech Republic||
|Estonia||
|Georgia||
|Hungary||
|Kazakhstan||
|Kyrgyzstan||
|Latvia||
|Lithuania||
|Republic of Moldova||
|Romania||
|Russian Federation||
|Serbia and Montenegro||
|Slovakia||
|Slovenia||
|Tajikistan||
|Macedonia, FYR||
|Turkey||
|Turkmenistan||
|Ukraine||
|Uzbekistan||
|-10%|0%<br>10%<br>20%<br>|
||Annualized change|

Source: FAO (2011) 65
Tab(e 2A: Share of Eastern Europe in EC Imports of Tvxtiles and Clothing and 1nricators of the Coverage and Restrictiveness of the MFA, 1985 - 1989 (percent)

|EAxporter|(I)<br>Import<br>Share|(11)<br>Restricted<br>Sfports<br>(trade<br>coverage<br>ratio<br>or<br>RESTI<br>TOT)b|(III)<br> <br>Imports<br>Subject<br>to<br>Binding<br>Restrictions<br>SIND/<br>TOTe|(IV)<br>Imports<br>Subject<br>to<br>Binding<br>Restrictions<br>BIND/<br>REST<br>8|CV)<br>Average<br> <br>Quota<br>Utilization<br>Rate|
|---|---|---|---|---|---|
|EAST EUROPEAN<br>FIVE<br>||||||
|1985<br>|6.24<br>|64.33|46.79|72.74|67.85|
|1986<br>|6.51<br>|62.52<br>|45.87|73.36|62.44|
|1987<br>|5.82|61.63|45.32|73.53|65.25|
|1988<br>|5.40<br>|55.00|38.80|70.54|51.96|
|1989<br>|5.30<br>|53.28<br>|36.71|68.90|49.15|
|1985-1989|5.80|59.14|42.50|**71.86**|**59.07**|
|Bulgaria<br>||||||
|19dS<br>|0.31|48.99|41.27|84.23|75.56|
|1986<br>|0.31|51.22|41.74|81.49|77.47|
|1987<br>|0.24|57.46|44.88|78.11|73.14|
|1988<br>|0.19|49.99|35.15|70.30|48.23|
|1989<br>|0.20<br>|42.11<br>|15.40|36.56|46.80|
|**1985-1989**|**0.24**|**49.96**|**36.01**|**72.07**|**63.86**|
|Czechoslovakia<br>||||||
|1985<br>|1.26|70.45|49.18|69.80|92.31|
|1986<br>|1.33<br>|68.62|52.01|75.79|88.53|
|1987<br>|1.13|71.72|53.52|74.62|87.74|
|1988<br>|1.00|65.29|44.15|67.63|63.07|
|1989<br>|0.94<br>|63.60<br>|41.01|64.48|60.12|
|1985-1989|1.11|67.94|48.00|70.65|**77.57**|
|Hungary<br>||||||
|1985<br>|1.32<br>|57.34|40.13|69.25|5'..61|
|1986<br>|1.47|54.97|40.59|73.85|56.87|
|1987|1.38|50.37|37.89|75.21|6056|
|1988|1.30|47.17|38.63|<br>81.90|.<br>4567|
|1989<br>|1.29|48.18|40.00|<br>83.01|.<br>44.49|
|1985-1989|1.35|51.26|39.42|76.91|**52.60**|
|PoLand||||||
|1985|1.25|57.78|40.94|70.86|57.23|
|1986<br>|1.41<br>|57.77|43.38|75.10|52.29|
|1987|1.36|55.26|43.22|78.2.|69.51|
|1988<br>|1.39|46.40|35.56|76.63|L6.26|
|1989<br>|1.37|44.39|32.28|72.71|63.23|
|1985-1989|1.36|**51.53**|**38.55**|**74.81**|**61.32**|

CmAt....
**Table A5: Regression results for labor indicators**

|VARIABLES|(1)<br>Labor Force<br>participation (Ref:<br>Inactive)|(2)<br>Labor Force<br>participation<br>(Ref: Inactive)|(3)<br>Unemployed<br>(Ref: employed)|
|---|---|---|---|
|Survey year: 2016|0.250<br>(0.163)|0.174***<br>(0.052)|-0.101**<br>(0.042)|
|Survey year: 2017|0.440***|0.590***|-0.250**|
||(0.122)|(0.110)|(0.097)|
|Inflation*Post|-0.138<br>(0.094)|||
|Food Inflation*Post||-0.207***|0.126**|
|||(0.070)|(0.051)|
|Conflict|0.000|-0.001|0.000744**|
||(0.001)|(0.001)|(0.000)|
|Respondent is a woman|0.000<br>(0.035)|-0.016<br>(0.033)|0.0474**<br>(0.020)|
|Education level of household head_Primary|0.022<br>(0.036)|0.039<br>(0.031)|0.029<br>(0.020)|
|Education level of household head_Secondary|-0.060<br>(0.068)|-0.054<br>(0.064)|0.0783*<br>(0.043)|
|Education level of household head_University|-0.036<br>(0.052)|-0.057<br>(0.056)|0.0803**<br>(0.035)|
|Education level of household_Other|-0.165**|-0.0793**|0.015|
||(0.075)|(0.033)|(0.015)|
|Age|0.0103***<br>(0.001)|0.0685***<br>(0.007)|-0.005<br>(0.003)|
|Age-squared||-0.000842***<br>(0.000)|0.000<br>(0.000)|
|Constant|0.152**|-0.695***|0.138**|
||(0.067)|(0.114)|(0.061)|
|Observations|3,838|3,838|2,011|
|R-squared|0.204|0.277|0.154|

*** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses.

29