rafmacalaba/datause-encoder-data huggingface dataset:
focuses more on city landscape planning and profits from auctioning city land. The conflicts between the two agencies could lead to land expropriation. Moreover, competition and shift in power among SOEs may also lead to expropriation and re-distribution of Allocated land, as demonstrated by ample anecdotal evidence.<sup>14</sup>

# _2.C The 2009 Shenzhen Urban Renewal Program_

Our study exploits a unique policy change in the city of Shenzhen, the youngest metropolis with the highest GDP per capita in China. Largely agricultural in 1979, the area now known as Shenzhen was converted into a city as China’s first special economic zone for experimenting with market-oriented reforms. Its subsequently spectacular growth led to a skyrocketing increase in the value of properties, which makes the old property rights arrangements inefficient. Farmers built constructions over their unentitled and collectively-owned land to accommodate rapidly rising housing need of workers and factories, while existing firms built new factories over Allocated land. By the end of 2009, of the 918 square kilometers of total construction area of Shenzhen, 40 percent was illegally occupied by squatters, and another 24 percent was Allocated land, mostly used by SOEs or privatized SOEs.

The urban land titling program aimed to address the urgent need of land resources faced by Shenzhen firms. Economic expansion led to quintupling of its construction area. However, the total supply of land in Shenzhen has been limited. Shenzhen’s geographical boundary was fixed by the central government in 1980, making it hard to expand by absorbing neighboring farmland, as other Chinese cities do.<sup>15</sup> To relieve land scarcity, on November 13, 2009, the Shenzhen government initiated the _Urban Renewal Plan_ . This program allowed all current users of untitled land who had been operating on their land for many years to obtain the land title after paying a fee based on the

> 14 For instance, one listed SOE, the Yingli Corp, was forced to move one of its factories out of Baoding city due to the redistribution of Allocated land. 15 As a result, Shenzhen city has the highest land utilization ratio (i.e., the built-up land as a percentage of the total land) among cities, more than 50%. In comparison, the utilization ratio of Hong Kong SAR, China, is merely 20%.

14
**Appendix Table 5:** 2SLS estimates of determinants of female enrollment in secondary madrasas <u>and schools in 2003</u>

||Dependen|t variable: tot<br>grades 6-10|al female enr<br>in Madrasa|ollment in|Dependent vari<br>enrollment ingra|able: total<br>des 6-10 i|female<br>n school|
|---|---|---|---|---|---|---|---|
|# of girls enrolled in BRAC schools in the union (in 2003)|0.002|0.001|0.003|0.002|0<br>0.001|0|0.001|
||(2.50)*|(1.74)+|(2.45)*|(2.17)*|(0.6)<br>(1.1)|(0.65)|(1.26)|
|Madrasa is co-ed (base cat: female madrasa)|0.168|0.144|0.163|0.141||||
||(5.66)**|(5.32)**|(5.46)**|(5.17)**||||
|Age of madrasa|0.003|0.002|0.003|0.003||||
||(5.28)**|(5.09)**|(5.41)**|(5.32)**||||
|School is co-ed (base cat: female school)|||||0.084||0.086|
||||||(8.77)**||(9.13)**|
|Age of school|||||-0.003||-0.003|
||||||(16.41)**||(16.50)**|
|Union has a govt. school|-0.103|-0.061|-0.032|-0.016|0.008<br>0.022|0.003|0.026|
||(1.83)+|-1.06|-0.66|-0.33|-0.39<br>-1.04|-0.14|-1.35|
|Urban|||0.325|0.215||0.063|0.081|
||||(4.20)**|(4.32)**||(1.65)+|(2.20)*|
|# of boys madrasas in the union in 1999||0.033||0.038|0.027||0.022|
|||-0.9||-0.98|-1.24||-1.16|
|# of girls madrasas in the union in 1999||-0.058||-0.068|-0.02||-0.019|
|||(3.37)**||(3.54)**|(2.29)*||(2.22)*|
|# of co-ed madrasas in the union in 1999||-0.01||-0.014|-0.011||-0.012|
|||-1.37||(1.76)+|(2.63)**||(2.72)**|
|# of boys schools in the union in 1999||0.007||0.017|-0.01||-0.019|
|||-0.22||-0.56|-0.71||-1.52|
|# of girls schools in the union in 1999||-0.02||-0.015|-0.014||-0.012|
|||(2.06)*||-1.55|(2.77)**||(2.35)*|
|# of co-ed schools in the union in 1999||-0.01||-0.012|-0.01||-0.011|
|||-1.4||(1.66)+|(2.76)**||(3.13)**|
|# of new madrasas in the union during 1999-2003||0.048||0.044|-0.006||-0.007|
|||-1.21||-1.11|-0.39||-0.43|
|# of new schools in the union during 1999-2003||-0.04||-0.047|-0.016||-0.016|
|||-1.45||(1.69)+|-1.55||-1.6|
|Average age of madrasas in the union||0||0|0||0|
|||-0.29||-0.31|-0.05||-0.01|
|Average age of schools in the union||0||0|0||0|
|||(1.97)*||-1.57|(2.76)**||(2.59)**|
|Constant|-0.226|-0.052|-0.32|-0.079|0.066<br>0.093|0.062|0.089|
||(1.83)+|-0.8|(1.99)*|-1.15|-1.03<br>(2.18)*|-0.99|(2.14)*|
|N|6271|5266|6664|5520|12744<br>12744|13196|13196|

20
So far, attention has been focused upon _direct_ links. It is equally plausible however; that migrant networks exist between trading pairs which pertain to a country (of birth) _k_ , which is neither the importing ( _k≠i_ ) nor exporting nation ( _k≠j_ ) i.e. that a _third-party_ effect exists which is driving the observed coefficients.<sup>27</sup> Continuing from the discussion in Section 3, these third party effects can be modeled as ln(MIGki*MIGkj) where _i≠j_ and _k≠i and k≠j._ This is another important contribution of this paper, since despite only 68 countries being chosen for analysis; the comprehensive migration dataset permits thirdparty effects pertaining to potentially all (178) countries of the world to be included in estimation (see Equation 11).

# 11.

Where additionally, ς1 is a vector containing the coefficients for the interactions of the third-party effects. Table 8 again presents the statistically significant point estimates for the third-party effects estimated in [11] while both omitting and including τij. The estimates of the indirect effects of migrants are more stable in comparison with the existing literature, whether or not pair-wise fixed effects are included in the regression. Prior to the inclusion of τij, there are 30 positive and 29 negative point estimates; suggesting in comparison with direct effects third-party networks are more likely to divert trade.

It is not as immediately obvious why pair-wise fixed effects should be implemented when testing for third-party effects. Felbermayr, Jung and Toubal (2009) argue that third-party effects should be exogenous to bilateral trade flows between countries _i_ and _j_ , since the migrants pertain to countries _k_ .<sup>28</sup> However, if migrants from a country k are believed to foster trade between countries _i_ and _j_ then there might be unobserved bilateral factors between _i_ and _j_ , which encourage migrants from _k_ to select into migrating into those countries in the first place. In fact, controlling for pair-wise unobserved factors again drastically alters the results, with only 15 point estimates surviving. In the case of third-party effects however, the direction of the bias from excluding controls for the unobserved pair-wise factors is far from clear, since many estimates are both biased up and down. This result contrasts nicely with the estimates of the direct results. In that case, the direction of the bias was on average positive, which might be expected since international bilateral ties are more generally „good‟ as opposed to „bad‟ and in turn these good relationships might be expected to be positively correlated on average with migrant networks. In the case of indirect networks however, the results suggest a far more complex relationship. Arguably, these complexities can only be identified with richer more disaggregated data.

> 27 Felbermayr, Jung and Toubal (2009) are credited as having been the first to highlight the difference between direct and indirect links.

> 28 Furthermore they argue that these third-party effects must operate though the transaction cost channel.

28
The positive and significant coefficient for _MRP_ suggests that NSS design may account for at least some of the discrepancy between the two data sources.

However, it is also important to note that the gap between the consumption aggregates from these two sources does not necessarily imply that the NSS overestimates poverty. Some of the gap is due to errors in NAS consumption, which is determined residually in India, after subtracting other components of domestic absorption from output at the commodity level. There are also differences in the definition of consumption, and there are things included in NAS consumption that one would not use in measuring household living standards.<sup>36</sup> Some degree of under-reporting of consumption by respondents, or selective compliance with the NSS’s randomized assignments, is inevitable. However, it is expected that this is more of a problem for estimating the levels of living of the rich than of the poor.<sup>37</sup> We will look into the implications of the growing drift between NAS and NSS consumption in regressions that use (sectoral) growth variables from the NAS.

# **3. Summary statistics**

India’s urban population share has risen steadily over the 60 years, at about 1% per annum (0.25 percentage points per annum) over the 60-year period; Figure 1 plots the urban population share. (The extent of the linearity is possibly deceptive given that interpolations are used between census years, as noted in Section 2.) As can also be seen from Figure 1, poverty incidence showed no significant trend up to about 1970, but fell after that. (The series for the two poverty lines track each other closely.) Both higher growth rates and a higher pace of poverty reduction are evident in the post-1991 period; Table 1 provides the trend rates of growth.

Much of the faster growth post-1991 has occurred in the “tertiary” sector of India’s economy, which is primarily services and trade. The growth rate of tertiary sector NDP per capita doubled in the post-1991 period (after 1992), from 3.1% per annum up to 1991 to 6.4%.<sup>38</sup>

> 36 For further discussion of the differences between the two data sources see Sundaram and Tendulkar (2001), Ravallion (2000, 2003), Sen (2005) and Deaton (2005).

> 37 There is evidence from other sources consistent with that expectation; see Banerjee and Piketty (2003) on income under-reporting by India’s rich.

> 38  These are OLS regression coefficients on time; the standard errors are 0.04% and 0.2% respectively.

11
interventions and some other situations (such as finding corpses) classified as homicides in the health data are not included in the police definition (Poverty Reduction and Economic Management Sector Unit Latin America and the Caribbean Region 2006).

To improve the quality of the data, we limit the sample to municipalities that have been consistently meeting the international standards in quality control, i.e. where no more than 10 percent of deaths remained unexplained within any given year of the analysis. While in any one year only about 1,200 of Brazil’s 5,564 municipalities failed to meet these standards, only 2,130 municipalities have been adhering to it consistently during our panel time frame (2004 – 2009). These 2,130 municipalities in our final sample cover thirty five percent of the population. They differ in several dimensions from the rest of the country. They are more likely to have had a DEAM by 2009 and are more densely populated. While their overall per capita GDP and per capita GDP from industry is lower than in the group of low-quality data municipalities, they have higher per capita GDP from agriculture. The municipalities with high quality mortality data appear to be spending more: per capita municipal expenditures are higher. However, additional spending seems to mostly favor health sector: while they spend more compared to the rest of the country on hospitals and basic health, they spend less on police: per capita expenditure on civil defense and likelihood of having a municipal police force are lower. The municipalities in our sample also have a higher percentage of women enrolled in Bolsa Familia, and the size of the average per woman transfer is also higher. These comparisons suggest that our analysis is limited to municipalities that are not representative of the entire universe of Brazilian municipalities. We will take this into consideration when interpreting our results.

Information on the dates when women’s police stations were established in various municipalities comes from three sources. The Secretaria Presidencial da Mulher<sup>5</sup> (SPM) provided a list of DEAMS, and 104 within our municipalities of interest had the dates of establishment. We found information for an additional 14 municipalities through the Internet. These dates were further supplemented by the MUNIC (Pesquisa de Informações Básicas Municipais) municipality survey run by Brazil’s statistical office IBGE (Instituto Brasileira de Geografia e Estadistica). In 2004, 2006 and 2009 municipalities were asked if they had a DEAM<sup>6</sup> . Notably, some of the

> 5 Ministry of Women. 6 Although the survey is annual, the question about DEAMs appeared in the survey only in 2004, 2006, and 2009.

10
- **Rendall, Michelle** , “Brain Versus Brawn: The Realization of Women’s Comparative Advantage,” _SSRN eLibrary_ , 2010.

- **Romalis, John** ,“Factor Proportions and the Structure of Commodity Trade,” _American Economic Review_ , 2004, _94_ (67-97).

- **Roy, Andrew D.** , “Some Thoughts on the Distribution of Earnings,” _Oxford Economic Papers_ , June 1951, _3_ (2), 135–146.

- **Santos Silva, J.M.C and Silvana Tenreyro** , “The Log of Gravity,” _Review of Economics and Statistics_ , 2006, _88_ (4), 641–658.

- **Segura-Cayuela, Ruben** , “Inefficient Policies, Inefficient Institutions and Trade,” 2006. Banco de Espa ̃na Working Paper 0633.

28
We illustrate our overall results in Table 7, which presents predicted average PM10 levels in cooking and living areas by income and women’s education.

**Table 7:  Average Predicted PM10 Concentrations (ug/m**<sup>**3**</sup> **) By Income and Women’s Education Level**

|||Co<br>|Wom<br>okingAr<br>|en’s Ed<br>ea<br>|ucation|Level(<br>|Range:<br>Liv<br>|0-4_<br>ingAre<br>|as<br>||
|---|---|---|---|---|---|---|---|---|---|---|
|Income Per Capita<br>($US /Day)|0-<br>1.00|1.01-<br>2.00|2.01-<br>3.00|3.01<br>+|Total|0-<br>1.00|1.01-<br>2.00|2.01-<br>3.00|3.01<br>+|Total|
|0-$.50|267|305|205|261|272|229|257|183|234|233|
|$.51-$1.00|314|222|220|206|260|269|193|196|185|225|
|$1.01-$2.00|267|205|168|162|211|230|187|163|158|191|
|$2.01-$5.00|266|212|151|148|188|236|198|152|148|179|
|$5.01+|373|135|150|135|163|308|147|157|147|166|
|Total|278|246|192|181|254|239|213|178|173|221|

Table 7 is generally consistent with our econometric results, while suggesting that some nonlinear effects are not captured by the linear regression model in Table 5. For example, education seems to have minimal effects on pollution for the lowest income group, but pronounced effects when income is higher. Similarly, increased income doesn’t reduce pollution consistently for the lowest education group, but it has a strong effect at higher levels of education. The combined effects of income and women’s education are sufficient to approximately halve PM10 pollution, from near 300 ug/m<sup>3</sup> in the poorest, least educated groups to around 150 in the highest-income, best-educated groups. As our sample composition statistics clearly show in Table 8, most of the individuals (and households) in our survey are in the four table cells associated with the lowest income and education levels.

**Table 8:  Sample Composition**

|Income Per Capita<br>($US /Day)|A<br>0-1.00|dult Female E<br>1.01-2.00|ducation Lev<br>2.01-3.00|el<br>3.01+|Total|
|---|---|---|---|---|---|
|0-$.50|1,703|455|81|52|2,291|
|$.51-$1.00|585|553|172|78|1,388|
|$1.01-$2.00|184|251|121|76|632|
|$2.01-$5.00|32|59|41|61|193|
|$5.01+|11|18|25|54|108|
|Total|2,515|1,336|440|321|4,612|

19
# **Poverty Reduction Impacts of Economic Growth**

The high poverty incidence in Haiti can be explained by a series of factors including national economic policies such as a lack of macroeconomic stability, good governance, and political stability. What would happen to extreme poverty rates if positive economic growth returned to Haiti? This is addressed in this section in the form of (i) agricultural sector growth alone and (ii) uniform economic growth to all sectors.

Table 5.5: Projected Poverty-Reduction Impact of Agricultural Growth in Haiti

|||Total Haiti|Metropolitan|Urban|Rural|
|---|---|---|---|---|---|
|||Esti|matedPovertyR|ate (P0)||
|P0 in 2001||48.9|19.7|56.3|58.1|
|2 l l i|1 year|48.5|19.7|55.8|57.5|
|percent rea annua per-capta<br>aricultural rowth for|5 years|47.3|19.7|54.6|55.9|
|g g|10years|45.6|19.7|52.8|53.6|
|5 l l i|1 year|48.1|19.7|55.3|57.0|
|percent rea annua per-capta<br>aricultural rowth for|5 years|45.0|19.7|52.6|52.6|
|g g|10years|41.6|19.7|49.3|47.8|
|10 l l i|1 year|47.3|19.7|54.6|56.0|
|percent rea annua per-capta<br>riltrl rth fr|5 years|41.8|19.7|49.8|48.0|
|agcuua gow o|10years|36.2|19.6|44.6|40.2|

Note: P0 for households (not individuals), based on per-capita income growth in agriculture income and self-consumption. _Source:_ Own calculations based on HLCS 2001.

For example, simulation exercises show that if income per capita in Haiti as a whole grew by 2 percent per year from 2001, the rates of extreme poverty would fall by only 3.3 percentage points after five years (Table 5.5). After 10 years, the gains would be greater, but the rate of extreme poverty would still be high at 42.2 percent. Even if the country was able to generate a record high growth rate resulting in 5 or 10 percent growth in per-capita income, this would need to be sustained for 10 years to bring the extreme poverty rates down to 33.5 and 22.9 percent respectively. The projected poverty impact of increased uniform economic growth is much greater in metropolitan areas than in other urban and rural areas. After 10 years of steady real economic growth of 2 percent annually, extreme poverty falls by roughly 18 percent in the metropolitan areas, and by 11 and 14 percent in urban and rural areas, respectively. The same pattern holds true for larger annual growth rates. One explanation is that poverty in Haiti is not only broad but also deep, as indicated above. More research is needed to address propoor growth in Haiti.

The projected poverty impact findings for metropolitan areas of increased uniform economic growth are much larger than in other urban areas and rural areas. After 10 years of steady real economic growth of 2 percent annually extreme poverty falls by roughly 18 percent in the metropolitan areas, while it falls 11 and 14 percent in urban and rural areas respectively. The same pattern holds for larger annual growth rates. One explanation is that poverty in Haiti is not only broad as we saw above but also deep (see above).

25
the diagnosis of the ENLACE test and the design of improvement strategies. In addition, the PAE technical adviser coached teachers on analyzing the ENLACE information to have a clearer understanding of how schools were assigned to PAE and the causes of poor performance within their schools.

Between January and March 2010, authorities at Colima’s Ministry of Education, together with school directors and selected teachers, developed a simple methodology to construct a detailed diagnosis identifying the academic weaknesses of their students based on ENLACE results.<sup>4</sup> The diagnoses were tailored to each school: the ENLACE test questions which more students answered wrong were collected by subject area, grade and classroom. Using personal identification numbers and a password, all teachers in Mexico had online access to a rich data set organizing the proportion of students in their class who answered an ENLACE question incorrectly. The website also indicated the area of knowledge and the relevant curriculum area for each ENLACE question, thereby providing teachers with concrete pedagogical direction to guide their efforts (see Figure 3 showing an example of the type of information provided by ENLACE).

Between February and May 2009, PAE’s technical advisers, school directors and teachers worked on a school improvement plan to address the problems identified during the diagnosis. The school improvement plan had to include clear medium- and long-term goals in terms of learning outcomes and a plausible strategy, involving teachers and parents, to reach them. The CMoE’s technical advisers would visit PAE schools three times a month to follow up on the implementation of the school improvement plans. However, the “awareness” period of the program was too short to change any of the fundamental inputs of the learning production function and it is, hence, capturing the accountability and diagnosis effect of the program (see Figure 2).

The second phase, or the implementation stage, of PAE started in September 2010. It consisted of pedagogical interventions and the monitoring of progress. With diagnoses and school improvement plans in hand, state authorities, school directors and teachers collaboratively implemented the school-specific improvement strategies which, broadly speaking, included one or more of the following four interventions:

1. Strengthening school-based management. This intervention draws on the experiences gained from the on-going national school based management program: PEC ( _Programa Escuelas de Calidad_ ) and AGE ( _Apoyo a la Gesti ́on Escolar_ ). It should be noted that all schools in Colima at one time or another participated in PEC or PEC-like program.

2. Redefining the role of school supervisors and providing them with training. In 2010, school supervisors in Mexico were not appointed through a competitive process and

- 4The methodology relied on public information generated by the Federal Ministry of Education (SEP).

6
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