rafmacalaba/datause-encoder-data huggingface dataset:
has a high rate of school dropouts and low educational attainment. According to the Second Integrated Household Survey (IHS-2), the biggest reason for dropout from school is financial (National Statistical Office, 2005).

Third, unlike many other districts, Zomba has the advantage of having a true urban center as well as rural areas. As the study sample was stratified to get representative samples from urban areas (Zomba town), rural areas near Zomba town, and distant rural areas in the district, we can analyze the heterogeneity of the impacts by urban/rural areas. Finally, while Southern Malawi, which includes Zomba, is poorer, has lower levels of education, and higher rates of HIV than Central and Northern Malawi, these differences are relative considering that Malawi is one of the poorest countries in the world with one of the highest rates of HIV prevalence.

EAs in Zomba were selected from the universe of EAs produced by the National Statistics Office of Malawi from the 1998 Census. The sample of EAs was stratified by distance to the nearest township or trading centre. Of the 550 EAs in Zomba 50 are in Zomba town and an additional 30 are classified as urban (township or trading center), while the remaining 470 are rural (population areas, or PAs). Our stratified random sample of 176 EAs consists of 29 EAs in Zomba town, 8 trading centers in Zomba rural, 111 population areas within 16 kilometers of Zomba town, and 28 EAs more than 16 kilometers from Zomba town.

After selecting sample EAs, all households were listed in the 176 sample EAs using a short twostage listing procedure. The first form, Form A, asked each household the following question: ‘Are there any never-married girls in this household who are between the ages of 13 and 22?’ This form allowed the field teams to quickly identify households with members fitting into our sampling frame, thus significantly reducing the costs of listing. If the answer received on Form A was a ‘yes’, then Form B was filled to list members of the household to collect data on age, marital status, current schooling status, etc. From this we could categorize the target population into two main groups: those who were out of school at baseline ( _baseline dropouts_ ) and those who were in school at baseline ( _baseline schoolgirls_ ) _._ 9
Table 2: Matched sample balance: Socio-economic characteristics (survey data)

||.<br>**All**|**N**|**Control**|**N**|**Treatment**|**N**|
|---|---|---|---|---|---|---|
|Muslim|0.13<br>(0.34)|959|0.13<br>(0.038)|551|0.12<br>(0.035)|408|
|Hindu|0.73<br>(0.45)|959|0.73<br>(0.041)|551|0.73<br>(0.040)|408|
|Buddhist|0.14|959|0.13|551|0.15|408|
||(0.35)||(0.032)||(0.032)||
|HH size|5.11<br>(1.98)|959|5.09<br>(0.10)|551|5.13<br>(0.11)|408|
|Female headed household|0.078<br>(0.27)|959|0.076<br>(0.012)|551|0.081<br>(0.014)|408|
|Age HH head|46.6<br>(10.3)|959|46.5<br>(0.57)|551|46.7<br>(0.57)|408|
|Head able to read|0.68<br>(0.47)|959|0.68<br>(0.020)|551|0.67<br>(0.022)|408|
|Head able to write|0.71<br>(0.45)|959|0.71<br>(0.021)|551|0.70<br>(0.020)|408|
|Years of education HH head|5.70<br>(4.77)|959|5.50<br>(0.25)|551|5.98<br>(0.25)|408|

Note: Standard deviations in parenthesis in first column (All); Standard Errors in parenthesis in other columns, clustered at the gram panchayat; _* p <0.10, ** p <0.05, *** p <0.01_ 30
A1.7 Gallup International Gallup International Association is a group of 49 marketing agencies around the world. Gallup was founded in 1947 and is headquartered in Switzerland. As of May 1999, Gallup International can be reached at httrp://www.iallup-international.com/surveyl.html.

In 1997, Gallup Intemational celebrated its 50th anniversary with a survey of around 30,000 people in 44 countries. Opinions were collected on a variety of topics including corruption in society. For corruption, Gallup asked respondents "From the following groups of people can you tell me for each one of them if there are a lot of cases of corruption, many, few cases or no cases of corruption at all: 1) politicians, 2) trade unionists, 3) public officials, 4) businessmen, 5) judges, 6) ordinary citizens, 7) clergy/priests, and 8) journalists." We construct a country index: of corruption in the public sphere as the average responses regarding politicians, public officials and judges. We assign a value of 2 to "a lot", a value of 1 to "many" and a value of 0 to "few or none". For each of these three categories, we construct a weighted average of these three numbers using the fraction of respondents in each category. We then construct a simple average across the three categories as the country index.

Table A1.7 Gallup Intemational 50th Anniversary Survey <!-- Start of picture text --> From the followingi groups of people can you tell me for<br>each one of them if there are a lot of cases of corruption,<br>many, few cases or no cases of corruption at all:<br>1) politicians<br>2) trade unionists<br>3) public officials<br>4) businessmen<br>5) judges<br>6) ordinary citizens<br>7) clergy/priests<br>8) journalists<br><!-- End of picture text --> 43
# **B Details on Household Data from the LSMS-ISA**

The World Bank Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMSISA) is a household survey program that provides financial and technical assistance to national statistical offices in Sub-Saharan Africa for the design and implementation of national, multi-topic longitudinal household surveys with a focus on agriculture. The LSMS-ISA-supported countries include Burkina Faso, Ethiopia, Malawi, Mali, Niger, Nigeria, Uganda and Tanzania. We use the data sets from Ethiopia, Malawi, Niger, Nigeria, Uganda, and Tanzania in this work.<sup>26</sup> More details on each country are included in the following sub-sections and details on samples are provided in Table 2.

A common feature of the LSMS-ISA-supported surveys is that each sample household receives a multi-topic Household Questionnaire that elicit comprehensive socioeconomic information that also allows for the construction of consumption and income aggregates. Households engaged in agricultural activities additionally receive an Agriculture Questionnaire that elicits comprehensive information on smallholder crop, livestock and fishery activities and that allows for the construction of plot-level indicators of land and labor productivity and input use, among others. Finally, while the key variables that drive each survey’s sampling design is household consumption and income, each survey provides a large sample of agricultural households in each round.

In our analysis, we only include households which did not move. Although the LSMS-ISA surveys follow individuals who “split off” and create new households, we do not include these movers in our analysis.

## **B.1 Ethiopia**

The LSMS-ISA data from Ethiopia includes three waves. Wave 1 (2011/12) includes 4,000 households in rural and small towns across the country (CSA, 2014). This initial sample was followed in 2013/14 and 2015/16 (CSA, 2015; CSA, 2017). Beginning in Wave 2 (2013/14) the survey was also expanded to include 1,500 households in urban areas.

The Wave 1 data is representative at the regional level for the most populous regions (Amhara, Oromiya, Southern Nations, Nationalities, and People’s Region, and Tigray). In Wave 2, in order to align with the existing Wave 1 design while ensuring that all urban areas were included, the population frame was stratified to provide population inferences for the same five domains as in Wave 1 as well as an additional domain for the city state of Addis Ababa. However, the sample size in both waves, is not sufficient to support region-specific estimates for each of the small regions (Afar, Benshangul Gumuz, Dire Dawa, Gambella, Harari, and Somalie).

> 26We intend to extend our analysis to include Mali. We do not intend to include Burkina Faso, due to issues with geo-reference locations which make its use incompatible with the project methodology.

105
the primary producer a decade ago. Production in Bolivia and Peru declined sharply over the period.

||**1990**|**1995**|**2000**|**2002**|**2004**|
|---|---|---|---|---|---|
|**Bolivia**|77,000|85,000|13,400|19,800|25,000|
|**Colombia**|45,300|80,900|266,200|222,100|148,900|
|**Peru**|196,900|183,600|46,200|52,500|70,300|
|**TOTAL**|319,200|349,500|325,800|294,400|244,200|

Source **:** http://www.unodc.org/pdf/WDR_2005/volume_1_web.pdf

# **Table 3: Cocaine production, 1990-2004 (selected years)**

Afghanistan and Myanmar accounted for over 90 percent of global production of opium in 2004 (4,570 out of 4,850 metric tons). This two-country dominance in opium production has occurred in every year since 1988 (when systematic estimates began), except for 2001 when the Taliban successfully cut Afghanistan’s production by over 90 percent.<sup>11</sup> Table 4 reports estimated global production of opium for various years between 1990 and 2004. As shown by the table, second-tier opium producers include Colombia, Laos, and Mexico. Pakistan, Thailand, and Vietnam comprise the third tier; once substantial producers in the 1980s and early 1990s, they now are almost insignificant.

It is useful to contrast this with cannabis, the other prominent psychoactive drug that has its source in a plant. U.S. production accounts for a substantial (though unknown) share of U.S. consumption, apparently much of it grown indoors. In Western Europe it is thought that the Netherlands accounts for a large share of the cannabis consumed in Europe.<sup>12</sup> Morocco and Mexico supply substantial quantities of cannabis resin and cannabis herb to Western Europe and the United States, respectively, but they are certainly not dominant. Cannabis’ exceptional status probably rests on four factors: the bulkiness per unit value, which raises smuggling costs substantially; the high dollar yield per acre, which reduces risks of detection per dollar of there are no estimates of the quantities produced in each region and how they differ in terms of alkaloid content. See Drug Availability Working Group (2003).

> 11 Even in that year, Afghanistan still supplied large quantities of opium and heroin to the world market out of stockpiles.

> 12 No credible estimates of either marijuana consumption in Europe or of marijuana production in the Netherlands are available.

6
and 5% levels for the years 1970 and 2002, the endpoints of our sample. Second, the Moran scatterplots accompanying the maps graph the level of income of the state against that of its spatial lag for the same period as a way of illuminating the relationships captured by the statistics. A significant positive slope suggests that rich states are found among rich (quadrant 1) and poor among poor (quadrant 3). Quadrants 2 and 4 represent cases where rich states are found among poor, or poor among rich respectively. In fact this is a less efficient and comprehensive way of presenting the information in the kernels but one which allows a clearer view of the relative position of the states. Finally, tables 4 offer more detail still by showing significance levels and signs of the Moran statistics at 5 year intervals across the sample for all three definitions of neighborhood. Three findings emerge.

First, in the early period, we confirm a cluster of poor states around Oaxaca, Guerrero, Puebla, Chiapas and Guerrero corresponding to the traditional “southern states.” These appear strongly in the maps and in quadrant 3 of the scatter plots, and table 4 suggests that this relationship has been getting stronger across time for all definitions of neighborhood.

Second, using the bands or the distance measure, table 4 confirms that the border states considered as a block clearly stand as a pole of high income levels that has become stronger with time. However, the lack of any significance using the connectivity measure confirms that the income levels of the next row of states are not correlated with those of the border and hence it may not be so useful to talk about the “north” more generally. Baja California Norte and Baja California Sur, and Sonora appear in the quadrant 1 of the scatter plots as well-off states in better-off neighborhoods, and hence might be seen as a well-off convergence cluster located in the north of the country along the US border. However, these correlations seem to slowly disappear by the beginning of the 1990’s and the North, as a spatial unity encompassing more than just the border states disappears. The higher income of the frontline states has not spilled over much to the next line.

In the center we find little in the way of convergence clusters. Most of the Central states are located in quadrants 2 and 4 almost suggesting a downward sloping line (if we abstract from the outliers) and a tendency for rich states to be found among poor and vice versa. The greater variance in income per capita of this region (see table 1) underlies the finding of a lack of spatial dependency: poor states such as Zacatecas, Michoacan, Hidalgo, 14
# **Appendix A- How low can we go?**

# **URBAN Rayon Estimates**

**Figure A1: Standard Error as Percentage of point estimate: Urban Area Azerbaijan, FTT(0) Comparing survey based stratum-level estimates to census based rayon-level estimates (62 rayon plus 11 districts in Baku)**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>Stratum ratio (from survey)<br>10<br>5<br>0<br>0 10 20 30 40 50 60 70 80<br>Ranking by(s.e/point estimate)<br>Percentage<br><!-- End of picture text -->

**Figure A2: Standard Error as Percentage of point estimate: Urban Area Azerbaijan, monthly mean adult equivalent consumption Comparing survey based stratum-level estimates to census based rayon-level estimates (62 rayon plus 11 districts in Baku).**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>Stratum ratio (from survey)<br>10<br>5<br>0<br>0 10 20 30 40 50 60 70 80<br>Ranking by (s.e./point estimate)<br>Percentage<br><!-- End of picture text --> 41
approximate<sup>17</sup> share of the privately-owned banking system that has a stock exchange listing for the equity holders. On the other hand, the number of rated banks is not significant (regression 1.4), in line with our expectation that the role of specialized information firms may be a weaker channel, especially for a sample that includes such a high proportion of developing countries. Of the available proxies other institutional indicators proposed in Table 3, only the size variable (population) proves to be significant (Table 5, regression 1.7) and it has the expected sign: small countries performing better.

It needs to be noted, of course, that these bivariate correlations are not very robust, and as such can be taken as suggestive only. This is not surprising, given the very indirect character of the proxies employed. In particular, GNP per capita is an even stronger (inverse) predictor of banking risk (Table 5, regression 2.2), and it displaces the private sector monitoring index – though this remains close to significant at the 10 percent level when included.<sup>18</sup> More important, there is no evidence of an interactive term (Table 5, regressions 2.3 and 2.5): there is no indication of a systematically different private sector monitoring index is equally effective in reducing risk in rich and poor countries.

Table <u>3:</u> _<u>Suggested assignment of preconditions to different sources of discipline</u>_

|Type of precondition:<br>Source ofdiscipline|Market and administrative<br>institutions|Political and policy stance|
|---|---|---|
|Depositors|Accountability of bank management<br>Size of country (small)|Deposit protection (low)|
|Equity and bond holders|Percentage of private banks listed|Government ownership share (low)|
|Information firms|Number of banks rated<br>Auditingrequirements|Foreign ownership share (high)|

Table 4: _Indicators of selected preconditions by income level_ _<u>(Developing countries, mean values)</u>_

|Incomelevel|<$2500|$2500-$5000|$5000-$8000|>$8000|
|---|---|---|---|---|
|Accountability of bank management|2.71|2.72|2.72|2.91|
|Small size of country|11|8|10|4|
|Percentage of private banks listed|0.33|0.45|0.50|0.69|
|Number of banks rated|0.6|3.8|2.0|4.1|
|Auditing requirements|0.72|0.78|0.63|0.82|
|No deposit protection|0.56|0.26|0.32|0.09|
|Government ownership share|0.09|0.29|0.25|0.13|
|Foreignownership share|0.43|0.33|0.46|0.52|

Note: For definition of variables employed see note to Table 5. Source: World Bank Regulation and Supervision Survey, except income level (measured in PPP, 2001) and size (population in millions): World Development Indicators.

17Because of issues of aggregation and double counting, this measure is a fairly rough approximation. See note to Table 4 for its construction.

18 Several authors (La Porta et al. 1999, Barth, Caprio and Levine, 2001) have previously noted the difficulties presented by including GNP per capita, potentially an endogenous variable, in regressions along with determinants of banking risk. Clearly there is a risk that the independent role of such determinants as the private monitoring index could be masked by such inclusion.

22
**Figure 2. More than two thirds of developing countries are on track or close to being on track**

<!-- Start of picture text --> Distribution of countries by level of progress toward<br>MDGs<br>On target Close to target Far from target<br>80%<br>89 82<br>70%<br>60% 47<br>55 66<br>50% 25 58<br>40% 18 38 5253 4648<br>30% 21 36 30 39 3537<br>20% 17 12 19 25 23 27<br>15<br>13<br>10%<br>0%<br>Source: Authors’ calculations based on data from the World Development Indicators database.<br>Note: A country is ―close to the target‖ if its distance to getting on target (that is, its gap of trajectory) is smaller than the average<br>gap of all lagging countries. Otherwise, it is ―far from the target‖ (that is, its distance is greater than the average gap).<br>Proportion of developing countries<br><!-- End of picture text -->

**_How the country pattern differs from the aggregate picture._** The reference unit matters. Simple country averages that give equal importance to each country qualify the global story, which uses weighted averages that give more importance to countries with large populations. This pattern can go in both directions, for examples:

- The progress in reducing world poverty and in meeting the goal is essentially the result of rapid advances by China and India, with the absolute number of poor people falling rapidly in China. Despite the progress on the poverty goal, the average shortfall of lagging countries at 39 percent is still the biggest among the MDGs. Among lagging countries in the bottom half, extreme poverty also has the largest average gap at 67 percent.

- In contrast, the average distance to becoming on target for under-five mortality is only 23 percent for lagging countries, somewhat less daunting than the global distance derived from the population of all under-five children.  Moreover, the top half of lagging countries is only 8 percent from becoming on target.

- Although the progress of maternity mortality lags the most at the global level, there are hopeful signs at the country level.  The average distance of the top half of lagging countries is only 11 percent to becoming on target.  That said, the average gap for all lagging countries is still high at 32 percent, second only to extreme poverty; and the gap of the bottom half of lagging countries, at 51 percent is second highest.

11
16 The household estimates in Table 9 suggest NCMS had a statistically significant positive impact on outpatient visits (a 23% increase), but only at THC level. The facility results in Table 12 are consistent with this, but imply that significant effects on outpatient visits occurred only among central THCs, not among general THCs. Both household and facility results also point to NCMS increasing inpatient spells (a 27% increase according to the household data). However, while the household results hint that the extra spells took place in county hospitals, the facility results suggest that they occurred in THCs, especially in general THCs. This may be because the household data are from a sample, and the facility data capture all facilities in provinces where NCMS was being piloted at this stage. The results in Table 12 suggest that the extra inpatient spells at THC level were achieved by increases in the rate of bed occupancy rather than by shorter lengths of stay.

The decile-specific results in Table 11 point to some interesting differential impacts of NCMS on utilization across income groups. In the poorest decile, no impacts on outpatient utilization are evident at all, the impacts being evident only in deciles 2-10. There are no impacts on inpatient utilization evident either for the poorest decile; statistically significant positive impacts are to be found only in deciles 3-10. There is, in fact, a hint that NCMS may have had some dampening effect on inpatient utilization among the second decile, but the evidence is not strong. Thus NCMS does not seem to have increase utilization among the poorest 10% of China’s rural population.

The facility results in Table 12 point to NCMS having a significant positive impact on the revenues of THCs but not of county hospitals. In the case of central THCs, the revenue increase is at least in part due to extra subsidies, while in the case of general THCs, the increased revenue is entirely extra ‘business’ revenue. NCMS appears to have increased expenditures by less than revenues in the case of central THCs, but by more than revenues in the case of general THCs. NCMS does not appear to have significantly increased (or reduced) the cost per case (unadjusted for casemix). The increased facility revenues and business income do not necessarily imply, of course, that households’ out-of-pocket payments _net of reimbursement_ have increased. Indeed, the rise in business revenue is consistent with a fall in out-of-pocket payments net of reimbursement. In the event, however, it appears that NCMS has had no statistically significant effect either way on _average_ out-of-pocket spending by households, overall or on any specific type of care (Table 10). Furthermore, there is no evidence that NCMS has reduced the outlays