rafmacalaba/datause-encoder-data huggingface dataset:
8 demands, so that they can provide readable input into the model (via the SAM). But, indirect effects through reduced provision of social services are not captured.<sup>9</sup>

# Model Structure

The model of C6te d'Ivoire contains 15 sectors, and has 7 tradable sectors and 8 nontradable sectors. Its distinguishing feature, relative to earlier general equilibrium models of C6te d'Ivoire'°, is an explicit focus on social dimensions of economic policies; impacts of policy changes on socio-economic groups, analysis of anti-poverty programs, incidence effects of tax subsidy programs. The model also separately identifies formal and informal sectors of the economy; a departure frohn previous _work."_ Tradable sectors in the model include: the agricultural sector, the traditional export 3ector, the non-traditional export sector, the first transformation sector (formal and informal), and the manufacturing (formal and informal) industries. Non-tradable sectors include: gas and electricity, construction (formal and informal), transport, financial services, other services (formal and informal) and government services. Given the importance of agriculture in C6te d'Ivoire, considerable detail is incorporated in the model for the agricultural sector. We disaggregate this sector into the food crop sector, the traditional export sector (which include coffee and cocoa) and the non-traditional sector (e.g. sugar,

# 9 For more details see Demery, Noel and Wahba (1991).

'° The first model of this type for C6te d'Ivoire was used for the preparation of the third Structural Adjustment Loan. See Michel and Noel (1984a,b) and World Bank (1987).

" For more details on the informal sector, see Chia et al. (1991a). Lambert, Schneider and Suwa (1991) also model the informal sector although it is represented as one sector only.
- Allen, T., and D. Donaldson (2020): ‘Persistence and Path Dependence on the Spatial Economy’ https://sites.google.com/site/treballen/research

- Bird, J. and A.J. Venables (2019), ‘ <mark>Growing a Developing City : A Computable Spatial General Equilibrium Model Applied to Dhaka. Policy Research Working Paper. 8762. World Bank, Washington, DC.</mark>

- Bird, J. and A.J. Venables (2020), ‘Land tenure and land-use in a developing city; a quantitative spatial model applied to Kampala Uganda’ with J. Bird, _Journal of Urban Economics_ , 119, 103268.

- Duranton, G. and D. Puga, (2004). ‘Micro-foundations of urban agglomeration economies’, in: J. V. Henderson & J. F. Thisse (eds.), _Handbook of Regional and Urban Economics_ , volume 4, chapter 48, pages 20632117, Elsevier.

- Duranton, G. and D. Puga, (2015). ‘Urban Land-use’, in: G. Duranton, J. V. Henderson and W. Strange (eds.), _Handbook of Regional and Urban Economics_ , volume 5, chapter 8, pages 467-560, Elsevier.

- Fujita, Masahisa, Paul Krugman, and Anthony J. Venables. (1999) ‘The spatial economy: Cities, regions, and international trade’ Cambridge MA: MIT Press.

- <mark>Fujita</mark> _<mark>,</mark>_ <mark>Masahisa and Hideaki Ogawa, (1982) “Multiple equilibria and structural transition of non-monocentric urban configurations”,</mark> _<mark>Regional Science and Urban Economics</mark>_ <mark>, 12.2, 161-196.</mark>

- Heblich, S., S. Redding and D. Sturm, (2020) “The Making of the Modern Metropolis: Evidence from London” _Quarterly Journal of Economics_ , 135(4) 2059 – 2133

- Henderson, J. V., Regan, T., and A.J. Venables, (2021), ‘Building the city: from slums to a modern metropolis’’, forthcoming, _Review of Economic Studies_

- Lucas, R.E. and E. Rossi-Hansberg (2003), ‘On the internal structure of cities’, _Econometrica_ , 70, 4, 14451476.

- Monte, F., S. J. Redding, and E. Rossi-Hansberg, (2018) "Commuting, migration, and local employment elasticities." _American Economic Review_ 108.12: 3855-90.

- Redding, S. J., and M.A. Turner, (2015). ‘Transportation Costs and the Spatial Organization of Economic

- Activity’, in: J. V. Henderson & J. F. Thisse (eds.), _Handbook of Regional and Urban Economics_ , volume 4, chapter 20, pages 1339-1398, Elsevier.

- Redding, S.J. and Rossi-Hansberg, E.A. (2017) ‘Quantitative spatial economics’, _Annual Review of Economics_ , 9(1), 21-58.

- Rosenthal, S. S., and Strange, W. C. (2003). ‘Geography, industrial organization, and agglomeration’. _Review of Economics and Statistics_ , 85(2), 377-393.

- Sturm, D.S, K. Takeda and A.J. Venables (2021) ‘How useful are quantitative urban models to analyse cities in developing countries? Evidence from Dhaka’

- Tsivanidis, N. (2019), ‘ <mark>Evaluating the Impact of Urban Transit Infrastructure: Evidence from Bogotá's TransMilenio’, unpublished.</mark>

27
# **Introduction**

Forced migration within Latin America was relatively rare after the end of the authoritarian governments in the 1980s. However, man-made crises like those resulting from unstable political regimes and increased violence, as well as natural disasters have increased the number of forced migrants in the region in the last two decades.

Chile’s immigrant population has increased from just over 4 percent in 2017 to an estimated 7.5 percent in 2021, one of the largest in the region. Nearly 30 percent of these arrivals can be considered forced migrants as they came from Colombia, Haiti, and Venezuela (INE, 2021). Arriving in a new country produces a wide array of challenges for migrants, ranging from social to labor market integration to a lack of knowledge of the available host state’s benefits (UNHCR, 2020). Most prior research on immigrants’ integration has focused on the adult population or the effects of migration on natives’ educational outcomes. The effects on the migrants themselves remain relatively unexplored(Cerrutti and Binstock, 2012). Thus the education of this new migrant population is a key research and policy question.

Understanding how young migrants are included in the host country’s educational system is a critical aspect of migrant populations’ long-term integration (Bandiera et al., 2019) and helps determine their chances of upward mobility (Chetty et al., 2017). Previous scholarly research has shown that immigration increases pressure on educational institutions in the developed world (Su ́arez-Orozco et al., 2011). For instance, in 2018 there were approximately 18 million children under 18 in the US with foreign-born parents (MPI, 2019). These arrivals put some pressures on the local education systems all around the country. Developing countries with similar or smaller flows of migrants are likely to be more severely affected, since most are underfunded and already struggle to meet local educational demands. In addition to pressures on the educational system, studies in continental Europe have demonstrated that there are cognitive differences between immigrant and native children (Hull and Norris, 2020) and that socioeconomic disparities reproduce among migrant and native students (Entorf and Lauk, 2008).

Education is considered a powerful tool to ensure the assimilation and future well– being of young migrants particularly those who were forced to migrate, as they are more vulnerable than other immigrants (IOM, 2020). Moreover, education is a powerful engine of social mobility. In Latin America, higher education graduates earn 100% more than high school graduates and are more integrated into the country’s labor market and social fabric (OECD, 2016). However, little is known about how well forced migrants 2
informal safety nets exist-community lending or grants- are vastly weakened by both the reduced income of the community and the simultaneous demands put on it.<sup>4</sup> From a purely theoretical view, the gains in social welfare arising from redressing the impacts of crises are not distinguishable from those of economic growth-in both cases the ability to smooth by borrowing allows a shifting out of the social utility curve. The fact that in the former case we borrow from the future to raise present utility and in the latter we may raise both is immaterial from the point of view of raising total discounted social welfare. It is as reasonable for governments and the World Bank to lend for safety net programs that mitigate falls in present income as for gains in future productivity.

# _Crisis-alleviation Programs_

Government's role can be seen as redressing the effects of the market failures that lead to missing financial or insurance markets necessary for this smoothing. States could simply lend money to the affected individuals or regions, but in general they have chosen to give grants that serve to increase the resources available today. This may be due to all the complexities that led to the absence of credit and insurance markets in the first placecostly monitoring and absence of collateral for example. Or it may be that society believes that some share of the socially optimal transfers to augment the permanent income of the poor are best earmarked for times when the necessary smoothing is impossible and hence the welfare gain from the transfer is highest.

Whatever the reason, if the amount of the grants does not fully offset the fall in income, the simple view sketched above suggests that the largest effect on welfare would be gained from dedicating them entirely to consumption today. Following this logic, emergency programs should be thought of as vehicles for transfers and the central measure of their success is their transfer efficiency -how much of resources earmarked for the emergency program actually arrive at the target population during the crisis. As

> 4For example, Korean households may have been less able smooth systemic shocks (the crisis) as well as idiosynchratic shocks. World Bank (2000).

_5_
# **Does Local Employment Growth Accelerate Exits From Social Assistance?**

Evidence From Brazil’s Conditional Cash Transfer Bolsa Familia<sup>∗</sup>

**Katharina Fietz**<sup>†</sup> **Matteo Morgandi**<sup>‡</sup> **Gabriel Lyrio de Oliveira**<sup>§</sup> **Luiz Henrique Superti**<sup>¶</sup>

Originally published in the Policy Research Working Paper Series on _January 2024._ This version is updated on _April 2025._ To obtain the originally published version, please email prwp@worldbank.org.

**JEL Classification:** H53, I38, J01, J21, J24, O12

**Keywords:** Conditional Cash Transfer, Labor Market, Employment, Graduation, Bolsa Familia

> ∗We would like to thank Pablo Ariel Acosta, Tiago Falcao Silva, Renate Hartwig, Marcela Iba ̃nez, Krisztina Kis-Katos, Jann Lay, Josefina Posadas, Joana Silva, Jan Stuhler, the Brazilian Ministry of Social Development, and participants of the 28th Annual LACEA-LAMES Meeting, the German and Nordic Development Conference, the BeNA Winter Workshop, the 14th Interdisciplinary Ph.D. Workshop Perspectives on (Un-)Employment, the Development Economics Seminar G ̈ottingen and the GIGA Internal Seminar for their valuable comments and suggestions to this paper. In addition, we thank the Brazilian Ministries of Social Development and Labor for data access. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The authors declare that they have no relevant or material financial interest related to the research described in this paper. All remaining errors are ours.

> †University of G ̈ottingen, GIGA; `katharina.fietz@giga-hamburg.de` (Corresponding author; Neuer Jungfernstieg 21, 20354 Hamburg)

> ‡The World Bank; `mmorgandi@worldbank.org`

> §University of S ̃ao Paulo; `gabriel.ldo@gmail.com`

> ¶University of S ̃ao Paulo; `luizhfcs@gmail.com`
# **1. Introduction**

In order to facilitate the attempt of countries to make the transition to the knowledge economy, the Knowledge Assessment Methodology (KAM) was developed at the World Bank (Chen and Dahlman, 2004, 2005). It is designed to provide an assessment of countries’ readiness for the knowledge economy, and identifies sectors or areas in which policymakers should focus their attention and make future investments. The KAM is currently being widely used both internally and externally to the World Bank, and frequently facilitates engagements and policy discussions with government officials from client countries. This rich database is also potentially useful for research by political economists and political scientists.

The KAM database includes variables such as tariff and non-tariff barriers, regulatory quality, rule of law, adult literacy rate, secondary enrollment, tertiary enrollment, researchers in R&D , patent applications granted by the USPTO, acientific and technical journal articles, telephones, computers, and internet users.<sup>1</sup> They are constructed for over 120 countries, and are available at different points in time.

Any formal analysis employing KAM data must confront the problem of which variables to choose and why. Rather than make these decisions in an ad hoc manner, we recommend “reducing” the set of KAM variables to a smaller set of variables without losing information contained in the full set of variables. Factor-analytic methods are concerned with precisely – this problem reducing the data in a way that parsimoniously represents essentially the same information contained in the many variables. The parsimonious set of variables is the set of “factors” to which the data in the large number of variables is reduced.

> 1Source: The Knowledge Assessment Methodology (KAM) website (www.worldbank.org/kam).

1
Appendix Table 5b: RDD estimates of gaps in teacher language test scores associated with teachers hired pre- and post-FPE (multiple <u>bandwidths).</u>

||Bandwidth:|1|2|3|4|5|6|7|8|9|10|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Pooled|Coef|-0.277|-0.164|-0.200|-0.141|-0.127|-0.106|-0.096|-0.129|-0.134|-0.107|
||SE|(0.177)|(0.134)|(0.113)|(0.098)|(0.089)|(0.082)|(0.074)|(0.069)|(0.065)|(0.062)|
||Pval|0.118|0.222|0.077|0.148|0.151|0.194|0.193|0.062|0.039|0.085|
|Kenya|Coef|-0.803|-0.270|-0.497|-0.414|-0.297|-0.220|-0.183|-0.141|-0.098|-0.098|
||SE|(0.414)|(0.290)|(0.239)|(0.204)|(0.186)|(0.172)|(0.153)|(0.143)|(0.132)|(0.131)|
||Pval|0.057|0.354|0.039|0.044|0.112|0.204|0.233|0.326|0.459|0.455|
|Madagascar|Coef|0.295|-0.023|0.061|0.004|0.057|0.025|0.110|0.081|0.119|0.075|
||SE|(0.264)|(0.185)|(0.168)|(0.146)|(0.135)|(0.122)|(0.117)|(0.114)|(0.105)|(0.100)|
||Pval|0.269|0.902|0.718|0.978|0.672|0.835|0.347|0.475|0.260|0.455|
|Mozambique|Coef|-0.707|-0.248|-0.256|-0.128|-0.092|-0.119|-0.084|-0.231|-0.279|-0.197|
||SE|(0.642)|(0.475)|(0.375)|(0.323)|(0.285)|(0.258)|(0.238)|(0.219)|(0.205)|(0.200)|
||Pval|0.280|0.604|0.498|0.693|0.747|0.647|0.723|0.293|0.175|0.326|
|Tanzania|Coef|-0.764|-0.301|-0.366|-0.292|-0.409|-0.340|-0.326|-0.371|-0.374|-0.260|
||SE|(0.397)|(0.284)|(0.239)|(0.209)|(0.182)|(0.168)|(0.148)|(0.135)|(0.135)|(0.121)|
||Pval|0.058|0.291|0.128|0.163|0.025|0.044|0.029|0.006|0.006|0.033|
|Togo|Coef|-0.931|-0.233|-0.100|-0.275|-0.187|-0.226|-0.099|0.019|-0.018|-0.112|
||SE|(0.900)|(0.645)|(0.537)|(0.471)|(0.447)|(0.435)|(0.400)|(0.379)|(0.360)|(0.327)|
||Pval|0.310|0.719|0.853|0.561|0.677|0.605|0.806|0.960|0.960|0.733|
|Uganda|Coef|0.605|0.220|0.058|0.204|0.184|0.165|0.078|0.015|-0.091|-0.104|
||SE|(0.325)|(0.275)|(0.249)|(0.216)|(0.207)|(0.190)|(0.180)|(0.166)|(0.152)|(0.151)|
||Pval|0.067|0.426|0.815|0.346|0.374|0.386|0.665|0.928|0.552|0.489|

Notes: Table reports coefficient estimates (Coef), Standard Errors (SE), and p-values derived from estimating Equation (1). Each estimate and associated standard error and p-value are the result of a separate regression model. Pooled model includes country fixed effects.

A-10
The production of biofuels could enhance rather than reduce food security by increasing productivity through changing farming practices. It could enhance local economy by generating jobs to local population and markets to agriculture commodities which do not have market access at present.

The high cost of finance (16% or more) is one of the key barriers to the development of any new and emerging industries in Zambia. One option to reduce the financial barriers is to find markets for biproducts of biofuel industry. One of the bi-products is GHG mitigation through the substitution of petroleum products with biofuels. For example, if GHG mitigation caused by increased production of biofuels in Zambia could be sold to carbon market at a price of US$20/tCO2, the total investment required by biofuel industry could be offset by more than 15% irrespective of the type of feedstock used for biofuels.

Regarding the selection of feedstock for biofuels in Zambia, several factors may influence depending upon the diverse interests of market participants. Medium to large scale investors are likely to target high yielding feedstocks such as sugarcane and oil palm to maximize earnings, while small scale participants are likely to engage in low input drought tolerant feedstocks such as sweet sorghum, cassava, Jatropha and castor which can grow in most parts of Zambia. Small scale participation might have a better fit to rural development policy of the Zambian government which “seeks to increase access to affordable energy in rural areas to reduce poverty and promote economic growth”.

# **References**

Asia-Pacific Economic Cooperation (APEC) (2010). Biofuel Costs, Technologies and Economics in APEC Economies. www.biofuels.apec.org/pdfs/ewg_2010_biofuel-production-cost.pdf Asia-Pacific Economic Cooperation (APEC) (2010). A Study of Employment Opportunities from Biofuel Production in Asia Pacific Economic Cooperation (APEC) countries.

www.biofuels.apec.org/pdfs/ewg_2010_biofuels_employment.pdf.

Barley D.A. (2009). “The viability of sweet sorghum as a bioenergy crop in Zambia”. MSc. Thesis, Faculty of Engineering, Imperial College London, SW7 2AZ, UK.

Biofuels Association of Zambia (BAZ) (2011). Information from various BAZ documents and meetings. (2006 – 2011).

Bio-Era (2009). “U.S. Economic Impact of Advanced Biofuels Production”. February.

Banco Nacional de Desenvolvimento Econômico e Social of Brazil (BNDES) and Centro de Gestão e Estudos Estratégicos of Brazil (CGEE) (2008). “Sugarcane-Based Bioethanol – energy for sustainable development”. 1st Edition. November. www.sugarcanebioethanol.org 14
# **Section 1: Introduction**

Firms finance investment through a combination of internal and external funds. Inducing investment from either source depends on some degree of legal protection. The willingness of firms to reinvest internally generated funds depends on protection of basic property rights (Besley 1995; Mauro 1995; Johnson, McMillan and Woodruff 2002a); the willingness of banks to lend depends on the ability to capture collateral pledged in support of loans (Levine 1998); and the willingness of outside equity investors to take a minority ownership position depends on protection against tunneling by insiders (Johnson, La Porta, Lopez-de-Silanes, Shleifer 2000).

The connection between the laws and the legal system on the one hand and financial market development on the other is well established in the literature. La Porta, Lopez-de-Silanes, Shleifer and Vishny [henceforth, LLSV] (1997, 1998) show that outsider shareholder rights are better protected in legal systems with roots in the English common law system than in the various civil law systems, implying variation in the ability of firms to access outside equity. Levine (1998) uses cross-country data to show that both creditor-friendly laws and higher quality legal systems are associated with better developed banking systems, implying variation in the ability of firms to access outside debt.

What is less well established in the literature is the relative importance of the legal system in generating internal and external equity investments. In this paper, we provide evidence on this question using data from a census of firms in Mexico. We use data on the legal form of the firms—proprietorships, partnerships and corporations—to examine the relative importance of the legal system on the size of firms owned by a single person 2
> where _F_ (.) is the normal cumulative distribution function. The x variables used in this exercise correspond to the three upgrading requirements mentioned in Table 1. Since these upgrading requirements were not strictly enforced, we used these upgrading requirements variables as a general indicator of the economic development level of a county in 1993 to predict the probability of a county being upgraded to a city in 1994.

We use the first and second moments of the three upgrading requirements as our specification to predict propensity scores. If they can pass the common support test: the covariates in each stratified block are balanced, then we will use this specification to predict the effects of city upgrading by comparing outcomes between treatment (cities) and comparison (counties) groups (Rosenbaum and Rubin, 1984). We find that there are no significant differences in covariates between counties and cities within each stratified block using this specification. We hence use this specification to estimate the city upgrading treatment effect on city-level and firm-level outcomes.

Since the public finance data set is a panel data set, we obtain one propensity score for each jurisdiction-year pair. Hence each county or city will have multiple propensity scores. To mitigate the potential problem of counties inflating their economic figures right before the upgrading (Li, 2011), we used the earliest data point possible from the public finance data set to conduct the matching exercise. Thus, a county or city’s 1994 predicted propensity score is used to match a city with its similar county.

Li (2011) pointed out that the rate of economic growth is one of the key factors in determining which counties can be upgraded to cities. Therefore, in addition to variables corresponding to the three upgrading requirements, ideally we should include the growth rate of GDP as a control in predicting upgrading probability. However, GDP at the county-level is only available starting in 1997 when there was a major statistical standard change in China. Before 1997, we have official statistics on gross value of industrial and agricultural output, and include these variables in our PSM model as an alternative measure of economic development at the county-level.<sup>2</sup> The propensity matching score method also assumes that there exists a region of ‘common support’, where the treated and control propensity scores overlap, and over which a robust comparison can be made. Cities that fall outside of the region of common support are disregarded and for these cities the treatment effect cannot be estimated With matching, the proportion of such cities is small (only two city-year observations using the specification that we described earlier fall into this case—see Figure 2). Since the region of common support is vastly improved (see Figure 3 for kernel density distribution comparison) and the balancing test is passed between the treatment and control groups, the estimated effect on the remaining cities can be viewed as representative.

> 2 Variables that can potentially have extreme values are transformed to logs to minimize distortions.

7