rafmacalaba/datause-encoder-data huggingface dataset:
population was exposed to poverty in at least one quarter of the year. If policy makers view the poor just as the 14.4 percent who on average were poor over the year, they will greatly underestimate the impact that poverty has on their populous. The difference between the status quo and our Rawlsian interpretation of the _ever poor_ measure is even starker when considering the distribution-sensitive gap measures. The _ever poor_ poverty gap is almost three times greater than the status quo poverty gap, and the squared poverty gap is more than three times greater than the _status quo_ squared poverty gap index.<sup>16</sup>

# **_[Insert Table 4 approximately here]_**

# **IV. Discussion**

During the earlier part of the previous decade (from 2002 to 2007), Jordan experienced rapid economic growth accompanied by large reduction in poverty (Jordanian Department of Statistics and World Bank 2009, Mansour 2012). And while the rate of economic growth declined sharply between 2008 and 2010, the poverty rate is reported to have continued to fall by about 5 percentage points (World Bank 2012). Despite the reported progress in poverty reduction, popular perception of the overall level of poverty is significantly more pessimistic (Jordanian Department of Statistics and World Bank 2009, Mansour 2012).

The literature has attempted to explain the disconnect between data driven findings and popular perceptions using various channels. A joint Jordanian Department of Statistics and

> 16 When considering the average shortfall of the poor from the poverty line (i.e. the ratio of the poverty gap to the headcount), we know from above the status quo shortfall is 19 percent. In contrast, the Rawlsian ever poor shortfall is 25 percent.

18
|Ethnic fractionalization|Ethnolinguistic fractionalization index (measures the<br>probability that two randomly selected persons from a<br>given country will not belong to the same<br>ethnolinguistic group). Source: Mauro, initially from<br>the Atlas Narodov Mira (Departnent of Geodesy and<br>Cartography of the State Geological comnmittee<br>of the<br>USSR, Moscow, 1964) and Taylor and Hudson (World<br>Handbook of Political and Social Indicators, 1972).|
|---|---|
|Middle class share|Share of quintiles 2-4, average 1960-1996.|
|Gini|Average of the period 1900-1996|
|Trust|Percentage of respondents in each nation replying "most<br>people can be trusted". Source: World Values Survey.|
|GDP per capita, PPP|World Development Indicators.|
|GDP per capita growth|Worldl Development Indicators.|

Table lb <!-- Start of picture text --> Institutional Indicators<br>Mean Standard Number of Range<br>Deviation Observaions<br>Voice and Accountability 0.352 0.92 82 -2.5 - 2.5*<br>Quality of the Bureaucracy 3.678 1.46 72 0 -6**<br>Civil Liberties 3.397 1.62 81 1 -7**<br>Property rights & rule-based govemance 3.232 0.82 56 1- 6****<br>Govemment Effectiveness 0.28t 0.94 78 -2.5 - 2.5*<br>Graft 0.2713 1.00 78 -2.5 - 2.5*<br>Law and Order Tradition 3.743 1.40 78 0- 6**<br>Political Instability and Violence 0.116 0.94 78 -2.6 - 2.5<br>Political rights 3.305 1.83 81 1 7***<br>Regulatory Burden 0.386 0.60 82 -2.5 - 2.5<br>Rule of Law 0.263 0.97 82 -2.5- 2.5*<br>* 2.5 represents best outcomes ** 6 represents lowest risk<br>*** I represents most free * 6 represents best policy<br><!-- End of picture text --> 15
America by market capitalization, and the fourteenth largest among developing countries. Relative to developed countries, the Colombian stock market is small – its size relative to the total value of U.S. listed companies at the end of our sample is 0.4%. However, MSCI indices typically include the largest companies and the most actively traded stocks in each country. The average market capitalization of our sample stocks is six times larger than the average size of other listed companies in Colombia. Similarly, the yearly traded value of our sample stocks is seven times larger than that of the other listed companies (Table OA2, Panel B). Moreover, in terms of market capitalization, the stocks in our analysis are similar the largest ten stocks in the Russell 2000 index – a group that has been extensively used to analyze many key question in finance, including the price impact of index recompositions, the role of institutional ownership on price informativeness, and the effect of passive investors on corporate decisions, among many others (e.g., Chang et al., 2014; Boone and White, 2015; Appel et al., 2016).

# **3 Empirical Analysis and Results**

We analyze three main issues related to equity index rebalancings. First, we measure the price impact of these episodes. Second, we analyze the behavior of investors in response to these episodes. Finally, we combine the information on prices and traded quantities to estimate the demand elasticity for stocks.

## **3.1 The price effect of index additions and deletions**

We measure the price impact of index additions and deletions by looking at the price dynamics of Colombian stocks included into or removed from MSCI international equity indexes. We calculate abnormal returns as the difference between the stock returns and the implied returns from a single factor model, _Rst − β_<sup>ˆ</sup> _sRt_<sup>_D/s_</sup> . The stock’s beta, _β_<sup>ˆ</sup> _s_ , is estimated using daily stock returns in the year prior to the addition/deletion event and the return of the COLCAP, a value-weighted index of the Colombian stock market. To avoid spurious correlation, we exclude the return of stock _s_ itself from the index, _Rt_<sup>_D/s_</sup> . Returns are measured in Colombian pesos in excess of the domestic deposit rate.<sup>11</sup> Figure 1, Panel A, depicts the average cumulative abnormal return (CAR) of stocks in our sample from 3 trading days before the announcement date onwards. Deletions are normalized to be additions by multiplying the CAR of removed

> 11We use the 90-day DTF rate, an interest rate composite, calculated as the weighted average of the interest rates on 90-day Certificates of Deposits offered by Colombian banks and institutions.

7
||Out of School|N/A|45.52|N/A|
|---|---|---|---|---|
||Enrolled in ECE|N/A|39.11|N/A|
||Enrolled in Primary|N/A|15.36|N/A|
|**Age parent believes chi**|**ld should begin school**||||
|(mean, sd)||4.53 (0.03)|4.48 (0.03)|4.50 (0.02)|
|(median,IQR)||5(4,5)|5(4,5)|5(4,5)|

_Note: All estimates weighted. Psychosocial stimulation ranges from 0 to 14. Parental distress sum score ranges from 0 to 5. The age parents believe children should begin school is open-ended and represents the child's year of age._ Table 2 provides the average child development Z-scores for children 0-35 months by key variables. Consistent with evidence from other LMICs, the average Z-score in our sample was 0.03 (95%, CI= -0.02, 0.09). Evidence from the CREDI Short-Form Validation Paper (McCoy et al., 2018) finds similar distributions for India and Nepal. Unadjusted OLS linear regression analyses were run on child-development Z-scores by each key variable independently. The p-values from these regressions are reported in column 5. Variables found to be positively associated with development included: living in an urban area, receiving high levels of psychosocial stimulation, having a mother with a primary education or above, having parents who experienced no parental distress, and living in a household with no food insecurity. Paternal education and gender were not associated with development.

**Table 2. Development for Children Under 3 Years of Age by Key Variables (N= 1,389)**

|**N**|**(unweighted)**|**Average**<br>**CREDI Z-**<br>**Score**|**95% CI**|**Linearized**<br>**Standard**<br>**Error**|**p-value**|
|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|**All children under 3**||||||
|0-35 months|1389|0.03|(-0.02, 0.09)|0.03||
|**Age (REF= <6 months**||||||
|<6 months|390|-0.16|(-0.29, -0.02)|0.07||
|6-11 months|269|0.34|(0.19, 0.47)|0.07|<0.001|
|12-17 months|201|0.13|(0.01, 0.26)|0.06|0.002|
|18-23 months|114|-0.32|(-0.45, -0.20)|0.06|0.075|
|24-29 months|304|0.09|(0.03, 0.15)|0.03|0.001|
|30-35 months|111|-0.02|(-0.08, 0.04)|0.03|0.072|
|**Child Gender**|||||0.155|
|Male|774|0.07|(0.00, 0.14)|0.04||
|Female|615|-0.01|(-0.10, 0.08)|0.04||
|**Region**|||||<0.001|
|Urban|615|0.16|(0.08, 0.24)|0.04||
|Rural|774|-0.04|(-0.12, 0.03)|0.04||
|**Province (REF= Punjab)**<br>Punjab|611|0.06|(-0.02, 0.14)|0.04||

15
holdings of other government entities are deducted and the remainder is attributed to holdings of euro-denominated bonds by foreign investors.

The rapid growth of mutual fund assets in the 1990s continued until 1999 when their assets reached 31 percent of GDP. At that time, while their holdings of government securities had fallen in both absolute and relative terms, their investments in equities had reached 40 percent of their total assets. At the end of 2000, mutual fund assets were equivalent to 25 percent of GDP, while their holdings of domestic bills and bonds, which included issues made by banks and public corporations, amounted to 24 percent of their assets. Mutual funds held less than 6 percent of total general government debt in 2000.

Insurance companies are still weakly developed in Greece. Their total assets correspond to 6 percent of GDP and they invest about 15 percent of their assets in government securities. Their holdings represent less than 1 percent of total government debt. Pension funds are better developed than insurance companies but their financial assets are not properly recorded. Government securities held by the central bank on behalf of several pension institutions (and other public entities) represented about 4 percent of total debt in 2000. Thus, domestic non-bank institutional investors probably held at the end of 2000 about 11 percent of total debt. The share of banks declined to 38 percent of total debt, retail investors accounted for 3 percent and other government agencies held 10 percent of central government debt. Thus, foreign investors had an estimated combined total of 38 percent in euro-denominated and foreign currency debt.

**Table 3.10: Holders of Government Debt, 1995-2000**

|(percent of total)|1995|2000|
|---|---|---|
|Banking System|43.9|38.4|
|Mutual Funds|3.9|5.4|
|Insurance Companies|0.8|0.8|
|Pension Funds|3.1|4.1|
|Retail Investors|20.2|3.1|
|Foreign Investors in euro bonds|0.0|14.3|
|Foreign Investors in other debt|20.6|23.7|
|Other Government Entities|7.4|10.2|
|Total|100.0|100.0|

Source: Estimated on the basis of Bank of Greece, Ministry of Finance, IMF and OECD data.

Before concluding this section, three other important aspects of public debt management should be briefly discussed. The first concerns the very high interest cost of a large public debt that has been issued at high interest rates. The interest cost was kept low during the era of financial repression when banks were forced to invest in treasury bills and interest rates were highly negative in real terms. But after 1985, interest rates that were highly positive in real terms were paid on government debt and this was reflected in a rising interest cost relative to GDP. The cost reached a peak in 1994 when it amounted to 14 percent of GDP, up from 4.8 percent in 1981. The interest cost declined significantly in the late 1990s but it still amounted to 8 percent of GDP in 2000 and 6 percent in 2003 (Table 3.11).

38
# **4. Data**

Having discussed the estimation strategy in the previous section, we briefly describe the data before moving onto the results of our impact analysis. We use school-level census data from Punjab EMIS for 2003-2005. Even though school census data exists for all three years (2003, 2004 and 2005), majority of our analysis is based on data from 2003 and 2005 censuses. The main reason for doing this is the following: despite the fact that stipend program started at the beginning of 2004, the usual lag in both implementation (supply side) and response from the intended recipients (demand side) means that the full meaningful effect could not have taken place until a year later, thereby making 2005 a more suitable post-program data point.

Table 2 presents aggregate enrollments in grades 5 through 9 by year, gender and stipend eligibility of districts. For any given year and grade, girl enrollments in public schools are much lower than boy enrollments in each of stipend and non-stipend districts as a group. Gender bias is more pronounced within low literacy stipend-districts. In terms of trends, growth between 2003 and 2004 is prominent only for grade 6 girl enrollments in stipend districts. For grades 7 and 8, marked changes in girl enrollments are seen only by 2005, suggesting a lag in program impact, justifying our choice of 2005 as the end year of our impact analysis. Enrollment growth among boys are also large in both treatment and control districts, implying there are other forces in effect (not to mention other common reform components) in both areas since stipend are targeted to girls only. Grades 5 and 9 also record notable growth, but more so among girls and in stipend districts suggesting both “pull” and “push” effects of the program. Figures 2 and 3 show levels and growth rates, respectively, for combined enrollments for grades 6-8 by gender and stipend status.

EMIS data also has some useful school-level covariates such as infrastructure variables: rural/urban location, availability of water, electricity, toilet, and boundary walls. We generate yes/no (1,0) categories of each of these variables. We also calculate student-teacher ratio at the school level as the ratio of total enrollments to total number of working teachers. The extent of private schooling is proxied by ratio of private enrollments to public enrollment in a given locality. Data limitation makes us use private enrollment data from 2000 Census of Private Educational Institutions in Pakistan (PEIP 2000) and public enrollment from EMIS 2003. Ratios are calculated at the district level and provide an indicator for initial share of private sector that might affect growth in public enrollment later in time. Economic welfare indicator is measured by 15
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# **Table 2: Takeup of Financial Education**

||(1)<br>Attended Financial<br>Education Training at<br>SACCO|
|---|---|
|Autonomous Trainer Selection (T1)|-0.027<br>(0.018)|
|Gender (Male = 1)|0.025|
||(0.016)|
|Completed Primary School|0.072***|
||(0.015)|
|Age|-0.002***|
||(0.001)|
|Total Household Size|0.008***|
||(0.003)|
|R-squared|0.021|
|N|5148|
|Dependent Variable Mean in Fixed Trainer Selection(T2)|0.234|

This table presents analysis for takeup of the financial education program by household recipients. Data comes from the household survey and is restricted to the two treatment groups, T1 and T2, over two endline survey rounds. The regression controls for survey round dummies as well as stratification controls. Standard errors, clustered at the SACCO level, are reported in parentheses. Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
<mark>in Saleempur to 66.7% in Gauspur, with slightly over 50% of households owning improved structures with septic tanks. 34.7% households had pits with lining and slab, while 11.4% had simple pit latrines. 12% of the households in the sample received some amount of SBM subsidy to cover the cost of latrine construction.</mark> <mark>We conducted multiple focus group discussions with four distinct groups of latrine owners—female users, female non‐users, male users, and male non‐users. Each focus group discussion was conducted with a group of 6‐8 individuals from a particular village or</mark> _<mark>tola</mark>_ <mark>(neighborhood within the village), and discussed</mark> — <mark>the current state of latrine access and use, barriers structural, attitudinal, and normative—that influence or impede latrine use (own and for others), and notions of cleanliness (and its importance) and</mark> _<mark>gandagi</mark>_ <mark>.</mark> <mark>The baseline survey was administered at the household level, across 204 households that owned latrines, between January to February 2017. Households were identified by enumerators with the help of key individuals in the village and through a snowball sampling method. Standard in‐field randomization (following a “right‐hand” rule) was initially used to select a sample of eligible households for the survey. However, given the difficulty in finding households with latrines where at least one male or female adult was available, this method of selection was adapted when necessary. Table 2 summarizes key demographic characteristics of households and respondents (one from each household) at baseline.</mark>

**<mark>Table 2. Demographic Characteristics</mark>**

|**Variables**|_A_|_verage_|_Observations_|
|---|---|---|---|
|_Caste_||||
|General|5%|||
|Minority|14%|||
|Other Backward Classes|60%||204|
|Scheduled Castes|21%|||
|Average HH income (Rs.)|11,444|||
|Latrine being used (observed)|89%|||
||Male|Female||
|Gender|67%|33%|204|
|Average age|47 years|33 years|204|
|Married|84%|69%|204|
|Education: below high school|38%|47%|204|
|_Usually defecate in latrines_||||
|All HH members|76%|84%|1564|
|Respondents only|66%|71%|204|

<mark>The baseline survey measured socio‐economic characteristics, latrine access and use, structural barriers, beliefs and social norms regarding open defecation, narratives around cleanliness and purity/impurity, and the social reference network of respondents in each village. The last involved asking respondents about specific individuals with whom they interacted on a regular basis as well as about individuals in the village whom they considered influential. To elicit the relevant reference groups in the area, respondents were also asked about the frequency with which they interacted with people from their own</mark> _<mark>tola</mark>_ <mark>, village, and caste group. In addition to self‐reported data on latrine use (which also included place and time of last defecation to capture variability), visual inspections by enumerators, including photographs, were also used to gauge the functionality, use, and cleanliness of latrines.</mark> 7
immediate consumption boom following the distribution of reinsertion allowances.<sup>9</sup> As a corollary, the price effect and the externalities generated by investments of DDR money should be marginal except if investments were concentrated in specific domains. The in-kind allowances, which were distributed as part of the reintegration package, are likewise expected to have a positive but small impact on consumption in the long run.

In accordance with this line of reasoning, we propose to test the following hypotheses:

- _Hypothesis 1._ Reinsertion allowances received by demobilized combatants had a positive impact on the levels of consumption and investment of their household.

- _Hypothesis 2._ The short-run impacts on consumption of beneficiary households were short-lived. The impacts on investment lasted in the long run

- _Hypothesis 3._ Nonbeneficiaries were also indirectly affected by the program. This indirect impact depends on the main occupation of households.

- _Hypothesis 4._ Higher demand for normal goods induced a price increase in the short run.

By testing these hypotheses, we contribute not only to the scarce literature on the impact of demobilization programs, but also to the literature on the impact of cash and in-kind transfer programs on beneficiaries, nonbeneficiaries and prices.

# **3. Empirical Analysis**

# _3.1. The Data_

The empirical analysis draws on different types of data. The first dataset consists of a panel of households. The second dataset is a community questionnaire. The third dataset consists of administrative data from the National Commission for Demobilization, Reinsertion and Reintegration.

The Panel Dataset. The first dataset constitutes a two-round household survey undertaken in Burundi. The first round is a QUIBB/CWIQ survey that is representative at the provincial level<sup>10</sup> collected in February 2006. The survey was requested by the Ministry of Planning and designed by the World Bank. Data collection was coordinated by the University of Burundi. The CWIQ Survey Technology has been applied in different African countries for the purpose of generating standardized indicators of poverty and welfare. Developed by the World Bank

> 9For example, in their evaluation of Progresa in Mexico, Gertler et al (2012) estimated that for each peso transferred, households consumed 74 cents and invested the rest, increasing long-term consumption by only 1.6 cents.

> 10“Questionnaire des Indicateurs de Base du Bien-être” or “Core Welfare Indicator Questionnaire”.

9