rafmacalaba/datause-encoder-data huggingface dataset:
Figure B.13: Payment rates and exposure of untreated blocks above and below median 2019 compliance, 100 meters buffer <!-- Start of picture text --> Above Median Below Median<br>Payment All Payment Payment<br>rate rate Compliance rate Compliance<br>b=-0.018<br>(p=0.687)<br>b=.000<br>(p=.979)<br>b=-0.013<br>(p=0.745)<br>.05 .15 .25 .35 .45 .55 .05 .15 .25 .35 .45 .55 .05 .15 .25 .35 .45 .55<br>.55 .55 .55<br>.5 .5 .5<br>.45 .45 .45<br>.4 .4 .4<br>.35 .35 .35<br>.3 .3 .3<br>.25 .25 .25<br>.2 .2 .2<br>.15 .15 .15<br><!-- End of picture text --> _Notes_ : This figure shows binned scatterplots of payment rates (y-axis) in pure control blocks by equally-sized bins of exposure to treated units within a buffer zone of 100 meters (x-axis). The left panel replicates the bottom left panel of Figure B.12. The middle and right panels split pure control blocks into blocks with above- and below-median compliance defined in 2019, respectively. The regressions flexibly control for a cubic polynomial of the number of properties in the buffer zone. This variable is highly correlated with payment rates, and its omission leads to omitted variable bias.

Figure B.14: Payment rates and exposure of untreated blocks and blocks with 80% treated units, 100 meters buffer <!-- Start of picture text --> Paymentrate October 2020 Paymentrate July 2020 Paymentrate August 2020<br>Pre intervention Pre intervention<br>Blocks with<br>80% treated<br>Untreated<br>blocks<br>.05 .15 .25 .35 .45 .55 .05 .15 .25 .35 .45 .55 .05 .15 .25 .35 .45 .55<br>.5 .5 .5<br>.45 .45 .45<br>.4 .4 .4<br>.35 .35 .35<br>.3 .3 .3<br>.25 .25 .25<br><!-- End of picture text --> _Notes_ : This figure shows binned scatterplots of payment rates (y-axis) by equally-sized bins of exposure to treated units within a buffer zone of 100 meters (x-axis). The left panel shows the gradient in both untreated blocks (blue dots) and blocks with 80% treated units (red triangles) for the October 2020 bill (the one affected by the intervention). The middle and right panels correspond to the pre-intervention bills of July and August 2020, respectively. The regressions flexibly control for a cubic polynomial of the number of properties in the buffer zone. This variable is highly correlated with payment rates and its omission leads to omitted variable bias.

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Table 13: CIWSLIY UE1UEEN CONSUIER FRICE INDEX (CP17) AND OtER FOODS IICPAMWI AND EVERAGE IICPAGBI PRICE INDICES

|||Independent|Variables||
|---|---|---|---|---|
||LnCPIG7 1/|LnCPlG7 2/||LnCPlG7I/|
|Intercspt|0.093<br>(0.048)|0.017<br>(0.025)||0.058<br>(0.029)|
|t-l|1.558|2.125||2.022|
||(0.263)|(0.183)||(0.182)|
|t-2|-0.363|-2.634||-1.488|
||(0.476)|(0.403)||(0.405)|
|t-3|0.023|3.752||0.661|
||(0.389)|(0.378)||(0.393)|
|t-4|0.004|-0.220||0.028|
||(0,212)|(0.242)||(0.240)|
|t-5|-0.205|-0.026||0.208|
||(0.133)|(0.125)||(0.144)|
|LnICPAGOFt-.|0.095<br>(0.029)||LnICPAG0t-2|-0.109<br>(0.019)|
|LnICPAGOF+-<br>2|-0.151<br>(0.038)||LnICPAGSt-2|-0.010<br>(0.019)|
|LnlCPA0FC.t3|0.072<br>(0.043)||LnICPAG8t-3|0.044<br>(0.021)|
|LnlCPAGOF-,|0.038<br>(0.022)||LnlCPAG8t,|-0.034<br>(0.017)|
|Sample|1954-88|1954-88||1954-88|
|n|35|35||35|
|R2|0.999|0.999||0.999|
|s.e|0.0127|0.0168||0.0148|
|2<br>(1) X(m)|2<br>X(5)|* 26.246|2<br>X25)|10.098|
|(2)<br>X2|X2g)|**a**6.682|X25)|*****30.253|

Notes: See footno es to Table 11.
9

**Table 4: Growth in Manufacturing and Non-Manufacturing Trade by Income Group and Region**

||Annua|l Growth in|Manuf. Tr|ade(%)|Annual Gr|owth in No|n-Manuf. T|rade(%)|
|---|---|---|---|---|---|---|---|---|
|CountryGroup|1980-90|1990-00|2000-10|1980-10|1980-90|1990-00|2000-10|1980-10|
|**BY INCOME LEVEL**|||||||||
|Developed economies|9.1|5.7|6.2|7.0|3.3|4.2|10.0|5.8|
|Developing economies|11.0|11.5|11.1|11.2|1.7|7.3|13.8|7.5|
|High income developing economies|14.9|9.6|8.1|10.8|0.6|7.3|13.0|6.9|
|Upper middle income economies|7.8|14.6|13.2|11.8|1.6|7.1|14.5|7.6|
|Lower middle income economies|8.7|9.8|12.7|10.4|3.5|7.9|13.5|8.2|
|Low income economies|5.8|7.0|12.4|8.4|2.5|4.3|14.1|6.8|
|Of which: Least developed countries|5.7|8.0|12.8|8.8|6.1|2.6|14.6|7.7|
|**Memo:**|||||||||
|Developing econ, excl. China & India|11.1|10.9|8.5|10.1|1.5|6.9|12.3|6.8|
|Upper middle income, excl. China|6.7|13.8|8.1|9.5|1.5|6.3|12.1|6.5|
|Lower middle income,excl. India|8.4|10.1|10.9|9.8|3.4|7.8|11.2|7.4|
|**BY REGION**|||||||||
|World|9.7|7.1|7.8|8.2|2.7|6.0|11.2|6.6|
|Developing economies|11.0|11.5|11.1|11.2|1.7|7.3|13.8|7.5|
|Asia|15.5|11.4|11.5|12.8|5.1|7.2|15.8|9.3|
|Latin America & Caribbean|3.7|16.6|7.6|9.2|2.4|7.0|11.9|7.0|
|Europe, Middle East & North Africa|3.3|6.0|15.1|8.0|-3.5|9.4|12.1|5.8|
|Sub-Saharan Africa|5.2|4.3|13.0|7.4|3.0|2.7|12.3|5.9|
|**Memo:**|||||||||
|BRICS 5 countries|11.6|10.9|18.3|13.6|8.3|3.9|18.7|10.1|
|OPEC 12 members|1.8|5.7|15.9|7.7|-2.8|8.9|11.5|5.7|

Note: The classification of manufacturing products in exports and imports is defined as SITC 5+6+7+8-68 in revision 3.

Source: Based on UN COMTRADE Statistics.
function was estimated correcting for the selectivity bias, none of the parental variables were significant in this equation. What is of more interest for the present discussion is that the returns to years of schooling were quite different from the uncorrected version of the earnings function, as follows:

**Table 5.3. Coefficient of 'years of schooling' in earnings equation, 1991-92.**

|||<br>Self-employed<br>J|Private employee|Public employee|
|---|---|---|---|
|OLS estimate|<br>.028|.040|.039|
|Estimate in equation|.030<br>|.067|.017|
|<br>with correction|I|||

_Source: Canagarajah and Liang (1996), Tables 6 and 8._ All the coefficients are highly significant.

The important result is that, after correcting for selectivity, the rate of return per year of schooling is found to be much higher in private employment. This is consistent with the changes in wage structure in Ghana discussed above. The recovery of the economy in the few years preceding 1991-2 had increased the importance of human capital in the market for wage labor in the private sector. At the same time the squeezing of wage differentials due to institutional factors in the public sector had, as already noted earlier, reduced the returns to education, particularly at the higher levels (see Canagarajah and Thomas (1997) and Dar et al, (1997)).

# **5.4 Trends in Child Labor and Schooling in Ghana**

One of the major constraint's in Ghana's growth challenge has been the lack of human capital development. The enrollment rates have not been picking up fast and the future trend of human capital does not look optimistic; in fact it is claimed the slack private sector investments in Ghana might be a result of lack of skilled labor and lack of adequate human capital stock. The non-school attendance rates in Ghana are very high with gender disparities. 1992 GLSS data indicate that one in three girls and one in four boys do not attend school. The rural non-schooling is higher with 37 percent for girls and 28 percent for boys. "Ghana 2000" in its strategy for accelerated growth has argued for massive investment in primary education as a way of building necessary human capital for sustainable broad-based growth (World Bank, 1993). It is therefore important to understand the dynamics of household decision making as to whether to send children to school. and/or work, to benefit from investments in education. If not, colossal public investments in education are not likely to get children into class rooms.

It has been shown that poor households in developing countries seldom find it viable to send their children to school; they are also in need of cash income. Hence parents do not send their children to school and allow them to work. This issue has been discussed in the context of Ghana by Canagarajah and Coulombe (1997) using GLSS 34
er%20Insurance%20Markets%20VIII_Hess.doc Istituto di Servizi per il Mercato Agricolo Alimentare (ISMEA) (2006). Risk Management in Agriculture for Natural Hazards, Hartell, J., H. Ibarra, J. R. Skees and J. Syroka, ISMEA, Rome. Available online at: http://www.sicuragro.it/Italia/Downloads/Derivati%20meteo%20Ismea%20%20Ver%20Inglese.pdf MDA Federal (2006). Creating Opportunities for a Scalable and Sustainable Weather Insurance Market for Smallholder Farmers in Africa Phase I: Upgrading the Malawi National Meteorological Service Observation Network. Prepared for the Commodity Risk Management Group, Agriculture and Rural Development Department, The World Bank, Washington DC, July 2006.

MDA Federal (2007). Creating Opportunities for a Scalable and Sustainable Weather Insurance Market for Smallholder Farmers in Africa Phase II: Installation of Weather Stations. Prepared for the Commodity Risk Management Group, Agriculture and Rural Development Department, The World Bank, Washington DC, June 2007.

Skees, J. R., Hazell, P. B. R. and Miranda, M. (1999). New Approaches to Public/Private Crop Yield Insurance in Developing Countries, Environment and Production Technology Division Discussion Paper 55, International Food Policy Research Institute, Washington DC, November, 1999.

Slater, R. and Dana, J. (2006). Tacking Vulnerability to Hunger in Malawi Through MarketBased Option Contracts: Implications for Humanitarian Agencies, Humanitarian Exchange, Humanitarian Practice Network, Overseas Development Institute, Number 33, March 2006.

Syroka, J. and Wilcox, R. (2006). Rethinking International Disaster Aid Finance, Journal of International Affairs, “The Globalization of Disaster”, Volume 59, 2, Spring/Summer 2006.

Vasquez, J. L. and Castellanos, L. (2007). Weather Business and Weather Risk Opportunities in Latin America. Presented at 9th Annual Weather Risk Management Association Annual Meeting, Miami, May 2007.

Weather Risk Management Association (WRMA) (2009). Results of the 2009 PwC Survey. Survey conducted by PricewaterhouseCoopers for WRMA, Washington DC.

World Food Programme (WFP) (2005). Pilot Development Project – Ethiopia Drought Insurance 10486.0, World Food Programme, Rome. Available online at: http://www.wfp.org/eb/docs/2005/wfp077056~5.pdf World Food Programme (WFP) (2007). Final Report on the Ethiopia Drought Insurance Pilot Project, World Food Programme, Rome, Italy.

World Bank (2004). Malawi Country Economic Memorandum, The World Bank, Washington DC.

19
|School attendance<br>rates of 0-3, 4-6, 7-<br>14, and 15-24 year<br>olds.|Brazilian Institute of Geography and Statistics (IBGE) -_Síntese_<br>_de Indicadores Sociais 2003 - Diretoria de Pesquisas,_<br>_Coordenação de População e Indicadores Sociais, Pesquisa_<br>_Nacional por Amostra de Domicílios_2002.www.ibge.gov.br<br>,<br>10/09/2004.|
|---|---|
|Unemployment rate<br>of 15-24 year olds|Brazilian Institute of Geography and Statistics (IBGE) -_Síntese_<br>_de Indicadores Sociais 2003 - Diretoria de Pesquisas,_<br>_Coordenação de População e Indicadores Sociais, Pesquisa_<br>_Nacional por Amostra de Domicílios_2002.www.ibge.gov.br<br>,<br>10/09/2004.|
|Proportion of 10-17<br>year olds who work<br>or work and study|Brazilian Institute of Geography and Statistics (IBGE) -_Síntese_<br>_de Indicadores Sociais 2003, Diretoria de Pesquisas,_<br>_Coordenação de População e Indicadores Sociais, Pesquisa_<br>_Nacional por Amostra de Domicílios_2002.www.ibge.gov.br<br>,<br>10/09/2004.|
|Proportion of 10-17<br>year olds who neither<br>work nor study|Brazilian Institute of Geography and Statistics (IBGE) -_Síntese_<br>_de Indicadores Sociais 2003. Diretoria de Pesquisas,_<br>_Coordenação de População e Indicadores Sociais, Pesquisa_<br>_Nacional por Amostra de Domicílios_2002.www.ibge.gov.br<br>,<br>10/09/2004.|
|Proportion of 16-24<br>year olds in the labor<br>force with formal<br>sector employment|_Ministério do Trabalho e Emprego/Relação Anual das_<br>_Informações Sociais (RAIS)/ Informações para o Sistema Público_<br>_de Emprego e Renda_– 2002.www.mte.gov.br<br>,03/09/2004.|
|Proportin of 16-17<br>year olds who voted<br>in the 2002 elections|_Tribunal Superior Eleitoral (TSE) – Eleições 2002/ Estatística do_<br>_Eleitorado por Sexo e Faixa Etária - Pesquisa por UF_.<br>www.tse.gov.br<br>,17/06/2004.|
|_Socioeconomic Status_||
|Share of people<br>below the poverty<br>line|IPEA (_Instituto de Pesquisa Econômica Aplicada_)._Dados_<br>_Regionais – Brasil – Indicadores Sociais – Renda -_2002.<br>www.ipedata.gov.br<br>|
|Average household<br>income per capita|IPEA (_Instituto de Pesquisa Econômica Aplicada_)._Dados_<br>_Regionais – Brasil – Indicadores Sociais – Renda -_2002.<br>www.ipedata.gov.br<br>|
|Share of workforce<br>with a signed work<br>contract (formal<br>sector)|IPEA (_Instituto de Pesquisa Econômica Aplicada_)._Dados_<br>_Regionais – Brasil – Indicadores Sociais – Mercado de Trabalho_<br>- 2002.www.ipedata.gov.br<br>|
|Number of physicians<br>per 1000 habitants|<br>IBGE,_Diretoria de Pesquisas, Departamento de População e_<br>_Indicadores Sociais, Pesquisa e Assistência Médico-Sanitária_-<br>2002.www.ibge.gov.br<br>|

18
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# **3.3 Rich countries' advantage in primary education**

Before proceeding we expand the left-hand-side of equation (1"') by an extra term, the share of primary education in total public spending on education. The equation for the disaggregation exercises is as follows: We ignore spending on student financial aid, as it is negligible at this level of education. We also exclude spending on pedagogical materials on the grounds that the percentages involved are small, ranging from 1.5 percent of total public spending on primary education, in the poorest countries to about 6 to 8 percent among OECD countries.<sup>2</sup> Data on the items in equation (3) for countries at selected per capita GNP in 1993 appear in table 5, the unshaded block containing items on the left-hand-side of the equation, and the shaded block, items on the right-hand-side. As before, each item is simulated from regression estimates of the relation between it and the per capita GNP. The simulations reveal a similar pattern as incomes rise: generally expanding total government spending, growing allocation to education, easing of the demographic burden, falling relative teacher salaries, expanding coverage and declining pupil-teacher ratios. Note, however, that as incomes grow, the share of primary education in total public spending on education falls, from 45 percent in countries at $200 in per capita GNP, to 33 percent in those at $10,000. Below we use the simulations to the compare the differences in education between pairs of countries at contiguous levels of per capita GNP.

> **_28_** That the omission is unlikely to affect our calculations is suggested by the results reported in table 2.1 relating to student aid. Spending on student aid as a percentage of total spending on education changed from 9.8 percent in one country to 5.4 percent in the other, but the change contributed only 1.8 percent to the total increase in resources available for school operations. By implication, incorporating spending for pedagogical materials, will make an even smaller contribution. Thus, to keep the computation **s** imple we choose to ignore this component of spending.
The combined electoral and criminal effects of forced displacement exacerbated already negative impacts on development and fueled the autocratic regime. Amid less pressure for reforms and more reliance on illicit activities and coercive enforcement, the incumbent government had few incentives to improve conditions for private and human capital investment. We leverage a mediation analysis to show that these two channels explain approximately 40 percent of the effect of forced displacement on development.

Our findings are robust to additional validity tests. The estimates are robust to alternative cluster specifications for standard errors, to approximating forced displacement using inverse distance to Colombia’s border crossing points, and to alternative difference-in-difference estimators (Abadie, 2005; Sant’Anna and Zhao, 2020). Another potential concern is the possible manipulation of electoral data. To address this, we conduct data checks for abnormal patterns and find no evidence of manipulation before 2018, in line with reports from the humanitarian and international organizations that validated those elections.<sup>6</sup> However, we do find evidence of manipulation (or the effects of a political boycott by the opposition) in the 2018 presidential election. For this reason, we include opposition-collected data for the 2024 presidential election, which indicate that Maduro lost by a landslide. Additionally, we analyze mayoral elections across 335 municipalities, comprising five election years and 1,675 observations. The scale of these elections makes systematic manipulation unlikely. The estimates remain consistent and support the validity of our findings.

We contribute most directly to recent research on the impacts of cross-border migration on migrant-origin socioeconomic conditions (Yang 2008; Dinkelman and Mariotti 2016; Theoharides 2020; Dinkelman et al. 2024; Godlonton and Theoharides 2025). Closest to our work, Khanna et al. (2022) and Testa (2021) focus on the effects of emigration on economic growth at origin locations. Our paper complements their work by examining the effects of forced mi-

> and corruption. The name derives from the sun insignias ( _soles_ ) worn by Venezuelan generals and the term is commonly used to describe the military’s role in facilitating and protecting drug shipments, particularly in cooperation with Colombian and Mexican cartels (U.S. Department of Justice, Office of Public Affairs, 2020; Transparencia Venezuela, 2022).

> 6Reports from the Carter Center (1998–2021) suggest no systematic manipulation before 2018.

8
geographic centers are not necessarily the economic or population centers that send and receive most migrants. Thus distance measures using geographic centers might not accurately reflect traveling cost between the more relevant economic centers.

Table 10 replicates the original results from Table 5 and confirms that the earlier results are robust to alternative measures of distance. ‘ _l.distij_ , geo. centroids’ is the geodesic (flight) distance between the geographic centers of districts _i_ and _j_ – it is the same distance measure used in the previous tables. Columns (1), (5), and (9) repeat the results from Table 5 for ease of comparison. We use three additional measures of distance: (i) ‘ _l.distij_ in columns (2), (6), and (10) is the flight distance between the economic centers of districts _i_ and _j_ , (ii) ‘ _l.TravelTimeij_ ’ in columns (4), (8), and (12 ) takes into account India’s transport network of national highways and measures the driving time on the shortest path between the economic centers of _i_ and _j_ ,<sup>19</sup> and (iii) ‘ _l.TravelTimeij_ , flat’ in columns (3), (7), and (11) assumes the same driving speed on and off the roads – this measure is similar to the flight distance between economic centers. The coefficients of all of these distance variables are negative. Furthermore in each case, the state border effect remains significant.

# **5 Discussion: Some explanations for the invisible wall at the border**

Why do state borders inhibit migration? In this section, we highlight a number of policies implemented at the state level which act as inhibitors, either explicitly or implicitly, of mobility across state boundaries. Three key inhibitors of inter-state migration will be discussed: inadequate portability of social welfare benefits and a significant home bias in access to education and public employment.

> 19See Alder, Roberts, and Tewari (2017) for more details on the method of computing these shortest paths.

21
**Figure 21: Type of Training Provided by Urban Formal Enterprises to Employees**

(Note: Of the 40% which reported providing some training) <!-- Start of picture text --> Combination of<br>methods<br>19% External provider<br>14%<br>On-the-job-<br>training by<br>outsiders<br>On-the-job training<br>8%<br>in-house<br>59%<br><!-- End of picture text --> Source: UES, 2004 63. (c) <u>Type of Training Institutions Most Valued: The survey results indicate clearly</u> that employers do not value any of the formal training institutions highly in Timor Leste, with even the best rated institutions (short course technical) valued highly by less than 40 percent of formal sector employers. Nonetheless, in relative terms, there are clear differences in the value placed on different types of training institutions. While hiring their employees, formal enterprises appear to value most highly the training their employees receive through short technical courses (Figure 22). Second in order of preference are the skills acquired at SLTA, followed by technical and vocational training centers and STM/High schools. Surprisingly, school education (at the high school level) ranks higher than polytechnics. In fact, only 17 percent employers reported that they consider Indonesian universities and polytechnics as important, and this figure was even lower for Timorese polytechnics (less than 10 percent).

64. The majority of enterprises also reported that they would welcome a standard certification system for different skills. This is an area where the Government has recognized a need and a draft law establishing a national training authority, which would be responsible for development of a certification system, is under consideration. With respect to certification, there has already been considerable work done by consultants to both the former L&S Secretariat and former MoEYCS which could inform the authority’s work.

65. The above results strongly suggest that it is difficult to justify the existing 3 year formal training courses in both post-school and technical high school settings, given that they appear to be so poorly valued by employers and that the unit costs of provision to the budget are high. Better labor market outcomes would appear to be achievable at considerably lower cost in many cases through increased reliance on shorter courses, often offered through non-formal institutions. To the extent that longer course are required, the result suggest a need to explore consolidation options for institutions or possibly seeking to send trainees abroad. These findings are consistent with earlier technical reports on the TVET sector in Timor Leste.<sup>10</sup>

> 10 See Abrahart, 2003 and 2004.

29