rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 7. Impact of performance-based grant on eligible junior secondary schools in Jakarta**

||**Jakarta v**<br>**metropolitan**<br>**secondary**<br>|**s other**<br>**area junior**<br>**schools**<br>|
|---|---|---|
||No control<br>(1)|Full controls<br>(2)|
|**Jakarta* Government*Year2015**|3.16***<br>(0.73)|5.28***<br>(0.89)|
|**Jakarta* Government*Year2016**|4.31***|5.01***|
||(1.07)|(1.07)|
|Observations|4,014|4,014|
|R-squared|0.618|0.622|
|No. of Jakarta government schools|280|280|
|No. of Jakarta non-government schools|613|613|
|No. of government schools in other cities|78|78|
|No. of non-government schools in other cities|367|367|
|S.D. of 2014 UN score in Jakarta non-government schools|7.2|7.2|
|P-value of difference in impact between 2016 and 2015|0.27|0.88|
|Years included|2014-16|2014-16|
||Jakarta,|Jakarta,<br>Bekasi|
|Cities included|Bekasi, Bogor,<br>and Tangerang|,<br>Bogor, and<br>Tangerang|
|Controls for students per classroom, teacher education and graduating<br>students|No|Yes|
|Time dummies and school fixed effects|Yes|Yes|

Notes: The dependent variable is national examination scores expressed as a percentage. Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Full results, including the coefficient estimates for the control variables, are reported in Annex Table A1.5.

There are a number of reasons why it is possible that the program had a different impact on primary and junior secondary schools. First, junior secondary schools tend to have more qualified and experienced staff and may have had greater capacity to improve examination scores compared to their primary school counterparts. Second, while the size of the incentive, in proportional terms was the same, in absolute terms it was much larger for junior secondary schools. Qualified junior secondary schools stood to receive IDR 110,000 from the performance component compared to only IDR 60,000 for primary schools.<sup>21</sup>

# **_Was the impact of the program different for high and low performing schools?_**

> 21 Indeed, comparing the impact of the grant by enrollment size in 2014 shows that the estimated impact in 2016 is bigger in size for larger junior secondary schools – those in the largest quartile registered a significantly different increase in scores (5.6 percentage points) than those in the smallest sized schools (3.4 percentage points). While estimated impacts for primary schools across enrollment sizes are negative, the effects for the largest two quartiles are significantly smaller in magnitude (less negative) than primary schools in the smallest quartile (see Annex Table A1.7.).

17
12

**Figure 2-4. Call for a strike of indefinite duration in Argentina**

Teacher unions and the strikes they proclaim account for various amounts of time loss worldwide. The conflicts between governments and the teachers unions may affect student performance. In Mexico it was found that union power is associated with test outcomes; while low and high influence of unions was not significant, medium power was found to be significant and had a relatively large negative effect.<sup>59</sup> In Argentina, adversarial political alignments were associated with a decrease in effective numbers of class days, with an indirect negative effect on student performance in Argentina.<sup>60</sup> _Source: Author, poster in Rio Negro department of education_ _Private schools for the poor._ The threat of prolonged teacher strikes and other forms of absenteeism drives parents to send children to private schools. (Many teachers also send their own children to private schools.) This is one reason why private schools for the poor have sprung up in the low-income areas of countries such as India. They often operate in squalid buildings and charge very little. Since they often operate without a license, authorities may not know about them. But visitors have found teachers present and students who seem engaged. This phenomenon has received attention and engendered calls to promote private schools and support them. Clearly, accountability to paying parents encourages the school owners to maximize instructional time, and thus improve students’ performance.

**Table 2-2. Provider Absenteeism Rates by Country and Sector**

||**_Absence R_**<br>|**_ates (%) in:_**<br>|
|---|---|---|
||**_Primary Schools_**|**_Primary Health Centers_**|
|Bangladesh|16|35|
|Ecuador|14|--|
|India|25|40|
|Indonesia|19|40|
|Papua New Guinea|15|--|
|Peru|11|23|
|Uganda|27|37|
|Zambia|17|--|

_Source: Chaudhury et al. 2004c; calculations are from facility surveys, except for Papua New Guinea (NRI and World Bank 2003) and Zambia (Das, Dercon, Habyarimana, and Krishnan 2005)._ 59. Alvarez et al. 2006 60. Murillo et al. 2002
# **Is Accra a Superstar City ?**

## **Robert M. Buckley and Ashna S. Mathema**

This paper was prepared as a background paper for a Public Private Infrastructure Advisory Facility (PPIAF) study of a Multilateral Investment Guarantee Agency-sponsored (MIGA) residential real estate development in Accra. Patrica Annez, Paul Asabere, Deniz Baharoglu, Charles Boakey, Jan Brueckner, Mila Friere, Sumila Gulyani, Olivier Hassler, John Hine, and Kyu Sik Lee made a number of helpful comments on earlier versions of this work. Carlos Calvacanti, Jerry Kalarickal and Fernanda Ruiz Nunez made particularly useful suggestions. The paper also makes use of surveys Ashna Mathema undertook in a number of other countries. The views expressed are not those of the World Bank, PPIAF, or MIGA.
# **7. Bibliography**

Ackello-Ogutu, Chris. 1997. “Unrecorded Cross-Border Trade between Kenya and Uganda.” _Technical Paper No. 59_ . Washington, DC: USAID.

Bhagwati, Jagdish. 1967. “Fiscal Policies, the Faking of Foreign Trade Declarations, and the Balance of Payments.” _Bulletin of the Oxford University Institute of Economics and Statistics_ 29:61–77.

Berger, Helge and Volker Nitsch. 2012. “Gotcha! A Profile of Smuggling in International Trade.” <u>Discussion Paper 2008/6, School of Business & Economics, Frei Universität, Berlin.</u> Econostrum. 2013, “Réduction du trafic de carburant : l’Algérie et le Maroc en vases communicants », available at www.econostrum.info , 3 September.

Fisman, Raymond and Shang-Jin Wei. 2009. “The Smuggling of Art and the Art of Smuggling: Uncovering the Illicit Trade in Cultural Property and Antiques.” _Applied Economics_ 1:82–96.

Haddar, Mohamed. 2013. Rapport sur les relations économiques entre la Tunisie et la Libye auprès du Plan Régional d’Environnement et de Développement Durable du Gouvernorat de Médenine (PREDD) et de la Table Ronde Économique (TRE) Médenine. Tunis.

International Crisis Group. 2013. _La Tunisie des frontières : jihad et contrebande_ . Brussels : ICG. Disponible à <u>http://www.crisisgroup.org/~/media/Files/Middle%20East%20North%20Africa/North%20Africa/Tuni sia/148-la-tunisie-des-frontieres-jihad-et-contrebande.pdf</u> Jean, Sébastien and Cristina Mitaritonna. 2010. “Determinants and Pervasiveness of the Evasion of Customs Duties.” _CEPII, Working Paper No. 2010-26._ Paris: CEPII.

Kaminski, Bartlomiej and Saumya Mitra. 2012. _Borderless Bazaars and Regional Integration in Central Asia._ Washington, DC: World Bank.

Leaders. 2013. “La contrebande en Tunisie: voyage au Coeur d’un systeme mafieux”, available at: <u>www.leaders.com.tn, July 7.</u> Leaders. 2013.2. “Tout sur le plan tunisien anti-contrebande”, available at: www.leaders.com.tn, July 11.

Meddeb, Hamza. 2012. “Courir ou mourir: Course à El Khozba et domination au quotidien dans la Tunisie de Ben Ali.” PhD dissertation, Institut d’Études Politiques, Paris.

Ministère du Développement Régional et de la Planification. 2012a. _Indicateur de développement régional: Étude comparative en termes de développement régional de la Tunisie_ . Tunis: Institut Tunisien de la Compétitivité et des Études Quantitatives.

Ministère du Développement Régional et de la Planification. 2012b. Indicateur de développement régional: Pointer les difficultés pour orienter les efforts et suivre le progrès: Gouvernorat de Médenine. Tunis: Institut Tunisien de la Compétitivité et des Études Quantitatives.

30
# **Exhibit 3a: Evidence from cross-regional studies (I/V)**

|**Study**|**Brief description**|**Time**<br>**period**|**Key findings**|**Implications for market**<br>**vs. intervention**<br>**question**|**Implications for centre**<br>**vs. region question**|
|---|---|---|---|---|---|
|Barro and<br>Sala-I-Martin<br>(1991)|• Convergence study<br>of 73 regions across<br>Germany, UK, Italy,<br>France, Netherlands,<br>Belgium, Denmark|1950-<br>1985|• Conditionalβ-convergence rates similar to<br>the US of about 2% per year<br>• Conditional convergence rates similar<br>across countries and regions|• NA|• NA|
|Boldrin and<br>Canova<br>(2001)|• 185 NUTS 2 regions|1980-<br>1996|• Neitherβconvergence norβdivergence for<br>income or labor productivity<br>• Nationalσconvergence but no regionalσ<br>convergence for income<br>• Absence of empirical support for increasing<br>returns and agglomeration theories<br>• EU regional and structural policies have little<br>relationship with fostering regional growth<br>• No evidence of relationship between<br>Structural Funds and productivity in Greek<br>and Spanish regions|• Absence of evidence of<br>increasing returns and<br>agglomeration in<br>combination with<br>evidence of little effect<br>of Structural Funds on<br>regional growth<br>suggests that<br>interventionist policies<br>inadvisable|• NA|
|Badinger et.<br>al. (2004)|• 194 NUTS 2 regions|1985-<br>1999|• Accounting for spatial effects in a dynamic<br>panel data model, they find convergence at<br>a rate of 7%<br>• High spatial correlation with neighboring<br>regions|• High spatial correlation<br>shows predominance<br>of market forces over<br>centralized<br>redistributionist policy|•NA|

**Evidence for market         Evidence for interventionist policy         Evidence for decentralized approach Evidence for centralized approach**

25
**Table 8: Fallow Field Determinants**

|Dependent Variable:|Fallow Fie<br>(1)|lds (% of Total)<br>(2)|Fallow Field †<br>(3)<br>(4)|
|---|---|---|---|
|**_Manager Characteristics_**||||
|Female †|0.0062<br>|0.0072<br>|0.0087*<br>0.0111**<br> <br>|
||(0.0076)|(0.0075)|(0.0052)<br>(0.0055)|
|Age (years)||0.0002<br>(0.0001)|0.0001<br>(0.0001)|
|Years of Schooling||0.0005|0.0004|
|||<br>(0.0007)|<br>(0.0007)|
|Manager Disability †||-0.0110|0.0024|
|||(0.0070)|(0.0081)|
|Hours per Week for Agriculture Activities||0.0000|0.0002*|
|||(0.0002)|(0.0001)|
|Access to Extension Program †||0.0028|-0.0003<br>|
|||(0.0082)|(0.0056)|
|Access to Credit Services †||-0.0033|0.0072|
|||(0.0056)|(0.0049)|
|**_Manager Land Tenancy_**<br>||||
|Log [Total Fields HA]||0.0105***<br>(0.0031)||
|Log [Field HA]|||0.0076***|
||||(0.0018)|
|Number of Fields Managed||0.0016***<br>|<br>0.0021***<br>|
|||(0.0006)|(0.0006)|
|Total Number of Crops Produced||-0.0032**|-0.0035***|
|||(0.0013)|(0.0012)|
|HH Parcel Certificate (% of parcels)||-0.0049||
|||(0.0055)||
|Parcel Certificate †|||-0.0048|
||||(0.0047)|
|Manager's Plot Occupation: Rented (% of parcels)||-0.0425***||
|||(0.0123)||
|**_Manager's Plot Characteristics_**||||
|<br>Average Slope (Total Fields)||0.0001<br>(0.0003)||
|Average Distance to Household (Total Fields)||<br>0.0000<br>||
|||(0.0000)||
|Field Slope|||-0.0000<br>(0.0002)|
|Field Distance to Household|||<br>-0.0001**<br>|
||||(0.0000)|
|**_Household Characteristics_**||||
|Log [Consumption (Birr)]||-0.0110*|-0.0016|
|||(0.0066)|(0.0043)|
|Distance to Closest Market (KM)||0.0003***<br>|0.0001*<br>|
|||(0.0001)|(0.0001)|
|Household Size||-0.0015|-0.0015|
|||(0.0011)|(0.0012)|
|Dependency Ratio||0.0060<br>|0.0051<br>|
|M th Hlf f th Hhld||(0.0045)|(0.0034)|
|ore an a o e ouseo<br>Production Sold †||-0.0057|-0.0070|
|||(0.0060)|(0.0069)|
|Non-agricultural Labor Income †||-0.0063<br>|-0.0055<br>|
|||(0.0049)|(0.0047)|
|**_Household Agro-Ecological Zone Classification_**||||
|<br>Troic-Warm/Semiarid †||00762***|00843**|
|p||.<br>(0.0291)|.<br>(0.0331)|
|Tropic-Cool/Semiarid †||0.0006<br>|-0.0007<br>|
|||(0.0154)<br>|(0.0170)<br>|
|Tropic-Cool/Subhumid †||0.0010<br>(0.0142)|0.0047<br>(0.0166)|
|TiCl/Hid||<br>00108|<br>00080|
|ropc-ooum †||-.|-.|
|||(0.0167)|(0.0170)|

31
**Figure 3.4: Log Income Densities for NE and SE Migrants in 1999**

<!-- Start of picture text --> o incom e of NE to SE migrants - - income of SE to NE migrants<br>- Income of all migrants<br>.8 A<br>.2~2<br>0 5 1t0<br>Income<br><!-- End of picture text --> _Note:_ Distribution of log-transformed monthly income for migrants over the last 10 years based on PNAD _1999._ **HouehAid heasi ynl. Pnnoilatinn** aged **I8 anri** anve.Etinnate **haceA nn Pnanierhniknv** kernel density estimates with a width of approximately 20. _Source:_ Author's own calculations.

# _Labor Market Participation_

Recent migrants into both areas are far more likely to be active in the labor market than their regior,al **countCrpaIs L I** **_aule_ 23.23).. V I", IatCs of emI p 0oy,C,11t** **_Iforl_** recent ad _IUlong-_ term migrants into both regions are slightly lower than regional averages, rates of participation (93 percent of recent N'r-SE and 85 percent of recent **SE-INrE** migrants are active in the labor force) are higher for recent migrants. Long-termn NE-SE migrants are about as active as the entire SE population in the labor market, but all migrants from the SE-NE are much more likely to participate than the NE population. SE-NE migrants are participating to a lesser extent than NE-SE mnigrants in the labor market. The percentage of inactive migrants (not part of the active population) is close to 16 percent for SE-NE migrants as compared to 7 percent for NE-SE migrants. Given that SE-NE migrants are on average sliehtlv older. this could indicate that a certain percentage of SE-NE migrants go to or return to the Northeast to retire.

Once migrants decide to participate in the labor force, there are only minimal differen **-- ---r te** **_of_ .tnlnnn-nt** **_a-co -4e to_ .ons and t,oon nrn.antc .-** **_and_ non=** migrants. In the NE, both recent and long-term migrants are employed at slightly lower rates than regional averages (tne employ-ment rate for migrants into the **iNE-** is about _92_ percent, while the regional average is around 95 percent). In the SE, a similar but slightly less pronounced pattern emerges.

Southeast to Northeast migrants appear to begin their employment in the infornal sector and, over time, shift to the formal sector. Formal sector employment for recent 13
# **4.1.1** **_Entry Model_**

The hypothesis of the entry model is whether the household head or member decides to start nonfarm enterprises given the rural investment climate in the community level. In other words, what are determinant factors for a household head to start a nonfarm business? These factors are further decomposed into household characteristics and community RIC variables. The entrydecision data are obtained from the household head’s decision to enter or not to enter nonfarm business during the last three years before the survey time in 2010. The Probit model is applied to assess which household characteristics and community level RIC influence the decision of entry (Deininger _et al._ 2007):

**_Z_** _i= 0+ 1 (_ **_H_** _i) + 2 (_ **_C_** _j) + 3 (_ **_D_** _j) + εi_ (1)

where _i_ denotes a household, _j_ denotes a community, **_Z_** denotes the household head’s entry decision (1: yes; 0: no), **_H_** denotes a series of household characteristics, **_C_** denotes a series of community-level RIC variables, **_D_** denotes governorate (province) and rural dummies, and _ε_ denotes an _iid_ error term. Variables included in **_H_** are household size, the head’s years of education, the head’s age, household agriculture income and other income. Community RIC variables included in **_C_** are hours to district market (access to market), percentage of electrified household (electricity), percentage of secure drinkable water available (water), percentage of high school educated (education), dummy for availability of physician (health), dummy for availability of management service (management), dummy for availability of formal financial institutions (access to finance), dummy for security (safety), dummy for disaster (disaster), and price of rice (price level). Variables included in **_D_** are dummies for Sana’a Governorate, Dhamar Governorate, and Rural/semi-urban.

Our results show no surprises regarding household heads’ decision of entry under household characteristics (table 4.1). The household heads with a larger family size are more likely to make the entry decision to nonfarm businesses. The less non-agriculture income households are also more likely to start businesses.

Access to water and the availability of business management services may help the households to start a nonfarm business because they are positively correlated with the decision of entry. The percentage of high school education in the community has significant negative correlation with the entry decision. This may reflect that for nonfarm entry the education level is not important, even uneducated people can start nonfarm businesses. Rural dummy indicates that rural households are more likely to make the entry decision than suburban households reflecting there are more opportunities for nonfarm businesses.

11
# **Appendix Table 9: Willingness to Pay to Avoid Disadvantageous Inequality and Willingness to Pay to Avoid Advantageous Inequality**

|<br>**Willingness to**<br><br>_Disadvan_|<br>**Pay to A**<br><br>_tageous I_|<br>**void Advan**<br><br>_nequality_|<br>**tageous Ine**<br><br>_Advant_|<br>**quality**<br><br>_ageous Ine_|<br><br>_quality_|
|---|---|---|---|---|---|
|Cntrl|Treat|Diff.|Cntrl|Treat|Diff.|
|Boys<br>0.18|0.19|0.01|-0.04|-0.01|0.03|
|(0.03)|(0.02)|(0.03)|(0.02)|(0.02)|(0.03)|
|Girls<br>0.13|0.26|0.13|0.05|0.06|0.01|
|(0.02)|(0.03)|(0.03)|(0.02)|(0.02)|(0.02)|
|Boys - Girls<br>0.05|-0.07|-0.12|-0.09|-0.07|0.01|
|_Notes_: This table estimates of th<br>(0.03)|e willingne<br>(0.03)|ss to pay of bo<br>(0.04)|ys and girls to<br>(0.03)|avoid disad<br>(0.02)|vantageous<br>(0.04)|

_Notes_ : This table estimates of the willingness to pay of boys and girls to avoid disadvantageous inequality and to avoid advantageous inequality, separately by treatment assignment. The willingness to pay to avoid disadvantageous inequality is the number of tokens the participant is willing to give up to _reduce_ the other child’s payoff by 1 token. The willingness to pay to avoid advantageous inequality is the number of tokens she is willing to give up to _increase_ the other child’s payoff by 1 token. The third and last columns show estimates of the treatment-control difference. The fifth row shows estimates of the boy-girl difference. All regressions include dummies specific for each childcare center × age group. Bootstrapped standard errors between parentheses (500 replications). _N_ Choices = 21,130. _N_ Participants = 2,113.

44
status increasing monotonically. This structure was used to minimize the effects of multidimensionality, which can lead to the perverse sequencing of the vignettes with respect to the intended ordering if respondents place different values on various characteristics contained in the vignettes. Such perverse rankings are inconsistent with our theoretical model (outlined below), which implies that respondents will agree on the ordering of the vignettes. This does not seem to be a concern for our vignettes since there were very few instances of “incorrect” ordering.<sup>9</sup> The most common characteristic of respondents who perversely order the vignettes is a low level of education of the household head. These cases of incorrect ordering were excluded from the analysis.

In Tajikistan, the subjective welfare experiment was embedded in the 2007 _Tajikistan Living Standards Measurement Survey_ (TLSMS). The sample consists of 4,860 households interviewed in September-November 2007. The sample is designed to be representative at the national, urban and rural levels, and at the _oblast_ (administrative region) level. Data were collected in two visits. In addition to the standard questions common to multi-topic household questionnaires, subjective welfare modules were developed and asked in the first visit.

In Guatemala, the _Impacto de las Remesas y la Migracion sobre la Seguridad Alimentaria y Nutricional_ (IRMSAN) survey was done in the department of Huehuetenango, Guatemala, located in the Western Highlands and bordering the Mexican state of Chiapas. This area is entirely rural and is characterized by high levels of both poverty and temporary outmigration. The survey has a sample size of 1,222 households interviewed from April-August 2008. The survey covered six micro-regions within the municipalities of Cuilco, San Gaspar Ixchil, Santa Ana Huista, and Jacaltenango—selected purposively to capture geographical heterogeneity. The survey was stratified to over-sample migrant households. Four months prior to fieldwork, a household census was conducted in the region. Probability weights are used in the analysis to adjust for the differing probabilities of selection.

In Tanzania, the _Kagera Subjective Welfare Survey_ was purposively designed for evaluating the potential use of anchoring vignettes in subjective well-being measurements. Fielded in November-December 2007, the survey consists of 450 households randomly selected from Ngara district of Kagera region on the border with Rwanda. This is a poor area in Tanzania

> 9 In Tajikistan, 89 of the 4,860 households in the sample had incorrect coding. In Guatemala the corresponding number was 28 out of 1,222 and in Tanzania it was 3 out of 450.

7