rafmacalaba/datause-encoder-data huggingface dataset:
# **_Stationarity tests_**

We have tested the following variables for stationarity:

- _Gexp_r_ - real total GG expenditure in 2000 US dollar

- _Cpub_r_ - real current GG expenditure in 2000 US dollar

- _Kpub_r_ - real capital GG expenditure in 2000 US dollar

- _Oilrev_r_ - real oil fiscal revenue in 2000 US dollar

# **<u>Table A.6. Nigeria: Unit Root Tests, 1970 - 2005</u>**

||L|evel|First|Difference|
|---|---|---|---|---|
||with intercept|With intercept<br>and trend|with intercept|with intercept and<br>trend|
|gexp_r|-1.51|-1.60|-4.03***|-3.96**|
|cpub_r|-1.80|-1.83|-4.71***|-4.63***|
|kpub_r|-3.13**|-2.90|-3.63**|-3.56**|
|oilrev_r|-1.64|-1.55|-5.08***|-3.53**|

_*: reject unit root at 10% level_ _**: reject unit root at 5% level_ _***: reject unit root at 1% level_ To avoid spurious regressions, we first checked the order of integration of the individual time series, used in the empirical analysis of the sources of volatility. All the series are integrated of order one, which means that their first differences are stationary (see table A.6). However, since there were indications for structural break at around 1984 in the three estimated equations, we would like to check the order of integration of the individual time series for the latter period; once again, we get the same results for the shorter period, 1984-2005 - the results in Table A.6a suggest that all variables are integrated of order one (see). Because all the variables are integrated of the same order, there is an indication that they are co-integrated and that a long-run relation between them exists.

**<u>Table A.6a. Nigeria: Unit Root Tests, 1984 - 2005</u>**

||Level|First|Difference|
|---|---|---|---|
||with intercept<br>With intercept<br>and trend|with intercept|with intercept and<br>trend|
|gexp_r|0.07<br>-1.61|-4.41***|-4.79***|
|cpub_r|-0.03<br>-1.32|-3.79***|-5.01***|
|kpub_r|-1.69<br>-3.98**|-4.27***|-4.28**|
|oilrev_r|1.33<br>0.02|-3.18**|-3.70**|

_*: reject unit root at 10% level_ _**: reject unit root at 5% level_ _***: reject unit root at 1% level_ To test for co-integration, we have performed ADF test on residual series from all three equations, as estimated above and shown in table A.3. Table A.7 shows the ADF results for the residual series from the equation on total GG real expenditure, current GG real expenditure and capital GG real expenditure. As seen from the table, the ADF tests decisively reject the null hypothesis that the residuals contain unit root, which is evidence that these series are co-integrated and that the OLS regression will not produce spurious results.

31
internal finance than new firms since information problems are likely to be lesser for existing firms than new firms. A similar analysis for U.S. firms could not be undertaken in the absence of data on the financing of project costs. In particular, this would have been useful in delineating the relative roles of internal and external finance--especially equity--in the financing of new capital expenditures vis-a-vis take-over of existing firms. It is well-known that the stock market plays a critical role in takeovers in the U.S. context and the two go in tandem.'

**(ii) Absorption of private capital issues:** Table 11 provides details of firms that issued shares to finance projects for the 1971-1993 period. This data reflects the booming of the Indian stock market in recent years. The number of issuing firms increased from 57 in 1971 to 121 in 1981 and 426 in 1993. Likewise, the amount issued has gone up from Rs. 423 million in 1971 to Rs. 984 million 1981 and Rs. 42 billion in 1993. One indicator of the stock market boom and maturity is the dramatic increase in the percentage of issues that were underwritten during the 1990s. Another interesting aspect is the composition of share ownership. For the 1971-93 period, of the 28 percent of issued capital that was subscribed, promoters contributed some 22 percent, while FIs took up the remaining 6 percent. Of the remaining 78 percent, the public subscribed to 53 percent while underwriters took up the remaining 19 percent, mostly as part of underwriting obligations.28 Also, the steady increase in the percentage of amount underwritten is another indication of the boom in the Indian stock market in recent years. It is

27 Among others, Mueller (1987) has attributed the wave-like pattern of mergers to this tight relationship between the stock market and take-overs.

**22** This is also consistent with the evidence in RBI (1995). Based on the analysis of capital issues for the 1986-87 to 1990-91 period, RBI found public response to equity issues to be higher compared to debenture issues, indicating the preference of investors to the risk capital in anticipation of getting higher returns.

25
31 As a result, in the four years of housing reform (in Moscow, 1992-1995; in Vladimir and Gorodets, 19931996), 14.2, 16.3 and 18.3% of the households changed their place of residence. If this had happened in accordance with the households' housing plans (see Fig. 2.3), mobility would have been lower in Gorodets, higher in Moscow, and the highest in Vladimir. The high mobility in Gorodets can probably be explained by the fact that residential movement was not always accompanied by an improvement in the households' living conditions, or it may have occurred due to migration (the findings of the survey do not contain enough data to verify these hypotheses).

Fig. 2.12 presents the proportion of households who have moved to other residences by average per capita income at the time of the survey. In Moscow during this period, household mobility in the top 20% group (fifth quintile) was substantially higher. As a result, over 1992-95, one-fourth of the wealthy households had moved to new housing. In Vladimir, the highest mobility was observed in the extreme income groups, while middle-income households mostly stayed put. In Gorodets, low mobility was observed only in the second income quintile, while in the others it was quite high.

Fig. 2.12. Household mobility (% of households who changed housing in the four years of housing reform, by quintiles based on per capita income) <!-- Start of picture text --> 35 - 3535<br>35<br>30 30 -<br>25 23.2 23 21 219<br>2 21 2 1.91925 23.3<br>20 - 3240 12 20 183 194<br>15 12 12-9 122 Iss101 11G 7<br>10<br>1 2 345 12 34 5 1 2 34 5<br>Moscow, 1992-95 Vladimir, 1993-96 Gorodets, 1993-96<br><!-- End of picture text --> Note: 1 - lowest quintile, 5 - highest quintile Sources: The Moscow Longitudinal Household Survey, Jan., 1996; Vladinir and<sup>Gorodets Longitudinal Household Survey, Dec., 1996</sup> Mobility (low or high) was largely determined by the progress of privatization (examined above in Section 1). At the end of 1996, the proportion of private dwellings was 42% in Moscow, 48% in Vladimir, and 66% in Gorodets. A direct link with the level of mobility is obvious enough. According to surveys conducted in these cities, owners of privatized housing exhibit a relatively low potential mobility<sup>as</sup> compared to households who rent housing from the state (Table 2.6).

Table 2.6. Household plans to move by owner/renter status, %

|!?cun-owe.yvs.<br>rented<br>.............<br>_ ..................................<br>_....|Moscow,<br>....<br>. _ .............................|1996<br>................|Vladimir,<br>...|1996|Gorodets,|1996<br>|
|---|---|---|---|---|---|---|
|<br>Privatized housing||11.7||21.0||13.0|
|Old single-family private houses||-<br>||12.5<br>||12.9<br>|
|State & municipal||26.2||26.9||17.9|

Sources: The Moscow Longitudinal Household Survey, January 1996; Vladimir and<sup>Gorodets Longitudinal Household Survey,</sup> December 1996.

The analysis of the factors involved in privatization makes this easy to explain. First, in the<sup>course of</sup> privatization the housing that first passed into private ownership was the best: for the time being,<sup>most</sup>
- Highest population growth is recorded in Pune followed by Hyderabad and the lowest in Delhi; the highest variation in population growth is recorded in Chennai.

- Smaller ULBs in Kolkata on an average have the highest population density with highest variation followed by Hyderabad.

**Table 4 Socio Demographic Characteristics: Smaller ULBs**

|Urban<br>Agglomeration|Area (Sq.<br>Km.)<br>Total<br>(Maximum;<br>Minimum)|Households<br>Total<br>(Maximum;<br>Minimum)|<br>Population<br>Total<br>(Maximum;<br>Minimum)|Growth rate<br>1991-2001<br>(%)<br>Average<br>(Maximum;<br>Minimum)|Population Density<br>(Persons per sq.km)<br>Average<br>(Maximum;<br>Minimum)|<br> <br>Literacy<br>Rates<br>(%)<br>Average<br>(Maximum;<br>Minimum)|
|---|---|---|---|---|---|---|
||580|410,180|1,899,081|67|3,276|78|
|Hyderabad|(103; 18)|(65,211; 19,748)|(292,289; 94,372)|(116; 20)|(10,770; 1,565)|(83; 63)|
||202|292,928|1,257,587|46|6,220|87|
|Chennai|(65; 17)|(174,145; 73,630)|(310,967; 76,093)|(1,118; 16)|(9,910; 3,529)|(92; 85)|
||705|1,652,518|7,865,180|47|11,144|84|
|Kolkata|(55; 3)|(211,441; 6,772)|(1,007,532; 33,858)|(459; -1)|(38,337; 1,835)|(94; 69)|
||86|94,079|427,260|8|4,970|NA|
|Delhi|(43; 43)|(69,034; 25,045)|(302,343; 124,917)|(39; -1)|(7,031; 2,907)||
||234|272,003|1,216,831|69|5,209|77|
|Pune|(171; 13)|(231,562; 9,773)|(1,012,472; 46,921)|(96; -3)|(5,938; 1,303)|(82; 73)|

**Source: Census of India, 2001, Authors’ Computations**

Water supply, sewerage/sanitation, solid waste management, roads and street lighting are the five core services chosen for this study. Most ULBs in India are responsible to provide these services. In addition to these services some ULBs have the responsibility to provide primary education and health care facilities. Apart from these a variety of other expenditures are incurred, common among these being expenses on general administration, slum rehabilitation, pensions, expenses for buying land, fire fighting, with slight variations across ULBs.

The status of five major services in the UAs are summarized in Tables 5, 6 and 7 below. The information presented in the tables suggests the following: 11
Table 6: Summary statistics at baseline (1992-1996), by mandate during which a first SHG was created

||First SHG crea|ted in the ward in||
|---|---|---|---|
||1997-2001<br>(Std. Dev.)|2002-2006<br>(Std. Dev.)|Difference<br>(Std. Err.)|
|**DEPENDENT VARIABLES FOR 1992-1996**||||
|Number of ward problems addressed by the WMs|1.42|1.36|0.06|
||(0.87)|(1.09)|(0.23)|
|WM deals with alcohol issues (%)|0.0|2.6|-2.6|
||(0.0)|(16.0)|(2.8)|
|WM deals with school issues (%)|6.1|7.7|-1.6|
||(24.2)|(27.0)|(6.1)|
|WM deals with forest issues (%)|18.2|23.1|-4.9|
||(39.2)|(42.7)|(9.7)|
|WM deals with public infrastructure (%)|78.8|71.8|7.0|
||(41.5)|(45.6)|(10.4)|
|WM deals with welfare schemes (%)|39.4|25.6|13.8|
||(49.6)|(44.2)|(11.1)|
|**CHARACTERISTICS WMs IN 1992-1996**||||
|**(**=**independent variables)**||||
|Male (%)|87.9|79.5|8.4|
||(33.1)|(40.9)|(8.9)|
|Education level (years)|4.9|6.8|-1.9<sup>_∗∗_</sup>|
||(3.1)|(3.7)|(0.8)|
|Land (acres)|3.0|3.2|-0.2|
||(2.0)|(7.8)|(1.4)|
|Number of children|3.7|3.1|0.6|
||(1.8)|(1.8)|(0.4)|
|Age|58.8|52.3|6.5<sup>_∗∗_</sup>|
||(11.1)|(12.2)|(2.8)|
|Caste category: SC (%)|6.1|7.7|-1.6|
||(24.2)|(27.0)|(6.1)|
|Caste category: OBC/FC (%)|12.1|25.6|-13.5|
||(33.1)|(44.2)|(9.4)|
|Number of wards|33|39||

> _∗∗∗_ significant difference at 1 percent, _∗∗_ significant difference at 5 percent, _∗_ significant difference at 10 percent. _Source_ : Authors’ analysis based on data from the Census of India, 1991.

38
|**Category**|**Vietnam yearly per capita**<br>**income(USD)**|
|---|---|
|(1) Unskilled workers in services|2547|
|(2) Skilled workers in services|5590|
|(3) Unskilled workers in agriculture|1391|
|(4) Skilled workers in agriculture|2294|
|(5) Unskilled workers in manufacture|1930|
|(6) Skilled workers in manufacture|4201|
|(7) Adults not in labor force|2113|
|(8)Elderly|1068|

The initial per‐capita revenue in each household _h_ can therefore be expressed as: Where ���� is an error term that depends on the household.

To build the scenarios, we proceed in three steps

- Reweighting of the households, to model structural changes in the population (age, education) and in the economy (share of adults in the labor force, share of working population in agriculture, manufacture and services)

- Productivity growth for each category of worker, applied to the �� . The error term also grows like the calculated income for each household.

- Income redistribution (optional)

- a. Reweighting

We re‐weights households to change the demographic structure of the country (number of people by age slice and skill) as well as its economic structure (number of adults in the labor force, number of workers in each economic sector).

As there is an infinite number of solutions for the re‐weighting process, we minimize the distance between current and future weights using a quadratic solver.

We exclude children from the re‐weighting (we keep the number of children constant) and instead we rescale the number of children afterwards so that the total number of children in the economy matches the new demographic structure. This allows, especially in Africa, creating new categories of households with the same adults but fewer children.

The composition of households therefore do not change – except for the number of children – but their weights in the population do. For instance, in order to increase overall education levels, the algorithm gives more weights to households with educated adults and less to households with uneducated adults.

46
of malaria cases<sup>3</sup> registered by NMCP in Gash Barka in 2008 was 20,320, which is about 3% of the estimated population living in the region (670,000). We tested 5,502 people in the survey, therefore the expected number of malaria cases among them over the whole year is 166, i.e. 3% of 5,502. Due to seasonality of malaria, the yearly share of malaria cases occurred in September<sup>4</sup> between 2002-2007 was 15%. Therefore the expected number of positive RDTs at the beginning of October was 25, i.e., 15% of 166. The number of positive RDTs in our sample is a bit larger than this, possibly because not all malaria patients report to health facilities.

# **B.2 Concern and knowledge of malaria**

To compute a measure of concern and knowledge of malaria, we build an index by using all available information on whether the respondent believe malaria is a concern in the community, are acknowledged of the malaria vector and are informed of the categories of individuals that are most affected by the infection. We average 16 dummy variables representing answers to these questions. For each variable, the respondent scores 1 if the answer is in line with concern or correct knowledge of malaria and 0 if the answer indicates wrong (or absent) knowledge of malaria. Therefore, an index equal to 1 indicates that the individual is concerned and fully aware of malaria. The variables considered in the construction of the index are presented in Tables B1 and B2.

The first set of dummy variables concerns knowledge about the vector (or the cause) of malaria. Table B1 presents the share of respondents in the control and treatment groups who mentioned each vector/cause of malaria<sup>5</sup> and the estimated effect of the IRS campaign. While there is widespread knowledge that mosquitoes are an important transmission vector, there is quite large share of respondents mentioning wrong causes such as unhygienic surroundings, poor diet or fatigue. A second set of variables indicates whether

> 3Sum of IPD (in patient department) and OPD (out patient department) cases.

> 4Positive RDTs indicate a malaria infection that occurred in the month prior to the test. September is roughly the month before the survey.

> 5The respondent is allowed to report multiple answers to this question.

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31
TABLE 4: Impact of changes in public spending on changes in the probability of accessing a health professional, program, or facility in one’s locality (by area)

||Pa|nel|
|---|---|---|
|_Dependent Variable_: Availability of**...**in your locality|Rural|Urban|
||IV|IV|
|General physician|-0.049|0.524|
||(0.070)|(0.128)***|
|Pediatrician/Gynecologist|-0.010|0.441|
||(0.018)|(0.112)***|
|Midwife|-0.002|0.330|
||(0.048)|(0.132)**|
|Child growth controls|-0.154|0.222|
||(0.051)***|(0.113)*|
|Public health center|0.178|0.212|
||(0.061)***|(0.127)|
|N|724|1235|

_Source_ : Young Lives Study Round 1 and 2, ENAHO 2002, 2006, and Ministry of Economic and Finance (2000–6).

_Note_ : Each coefficient represents a separate regression. Robust standard errors clustered by region-year in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%. Includes the average household wealth score in the locality, the population size, the average educational level in the region, a dummy for belonging to the highlands, a poverty index, the level of regional debt payments, regional fixed effects, and time fixed effects. Spending measured in 100 million soles. The instrument for all IV estimations is the amount of unanticipated mining revenues.

31
Turning now to absolute poverty, following past practice, “extreme poverty” is defined here by the fact of being poor by the standards used to define “poverty” in the poorest countries of the world. On that basis the World Bank’s current international poverty line is $1.25 per person per day at 2005 Purchasing Power Parity (PPP).<sup>12</sup> The latest (survey-based) estimate of the proportion of the population of the developing world living below $1.25 a day is 21% for 2010, down from 33% in 2000 and 43% in 1990 (Figure 3).<sup>13</sup> The overall poverty rate (“headcount index”) has been falling at a robust 1% point per year over 1981-2010. The regression coefficient of the poverty rate on the year in Figure 3 is -1.04 with a standard error of 0.05 (n=11). Figure 3 also gives the series for the poverty gap (PG) index. This is the mean gap below the poverty line, as a proportion of the line, expressed as a percentage. Equivalently, the PG index is the product of the headcount index and the income gap-ratio, given by the difference between the poverty line and the mean consumption or income of those living below the line, expressed as a percentage of the line. The PG index fell from 21% to 6% over the period 1981-2010; the regression coefficient of the index on year is -0.46 with a standard error of 0.04 (n=11). The income gap ratio also fell, from 0.41 to 0.31.

Progress has been uneven. Table 1 gives the regional breakdown of the headcount index for selected years. Figure 4 gives the series for three regions accounting for 95% of those living below $1.25 a day (in 2008). For East Asia, the rate of poverty reduction has been about double the average, at slightly over 2% points per year; for South Asia, it has been slightly less than 1%, and for Sub-Saharan Africa it has been about zero.<sup>14</sup> However, there are clear signs of a positive trend emerging in Africa in the 2000s, at a similar trend to South Asia. The late 2000s have seen progress against poverty—with both falling incidence and falling numbers of poor—in all six regions of the developing world (Eastern Europe and Central Asia, Latin America and the Caribbean, and the Middle-East and North Africa, in addition to those in Figure 4).

So the developing world as a whole has been maintaining robust progress against extreme poverty, judged by the $1.25 line, and there is clearly cause for optimism. However, as Chen and Ravallion (2012) also show, there has been less progress in getting people over $2 a day. A

> 12 This line is derived in Ravallion et al. (2009).

> 13 The estimates of absolute poverty measures used here are from Chen and Ravallion (2012), which discusses data and methods. (The update for 2010 gives 20.8% below $1.25 a day, representing 1227 million people.) A fuller discussion of the absolute measures (with various tests of robustness) can be found in Chen and Ravallion (2010); on the relative measures see Ravallion and Chen (2011).

> 14 The coefficients are -2.24% (s.e.=0.14) for East Asia, -0.88% (0.04) for South Asia and -0.09% (0.14) for Africa.

6