rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Fit Geographic. Regions.<br>agitPLA2it,<br>- Leasme Fie yf<br>eae i<br>Mountains wee aN“Lee Sloe gs fis oe<br>Shivalik (Chure hills) CEBEEE GB (B0<br>-<br>Source: Survey Department of Nepal<br><!-- End of picture text -->
# **When the Money Runs out:**

# **Do Cash Transfers Have Sustained Effects on Human Capital Accumulation?**<sup>*</sup>

Sarah Baird, Craig McIntosh, and Berk Özler Keywords: Cash Transfers, Long-term Impacts, Human capital JEL Codes: C93, I15, I21, I38, J12, J13

* Berk Özler (corresponding author), email: <u>bozler@worldbank.org, The World Bank; Sarah Baird, email: sbaird@gwu.edu, George Washington University; Craig McIntosh, email: ctmcintosh@ucsd.edu, University of</u> California, San Diego. We thank seminar and conference participants at the Center for the Study of African Economies, Columbia University, IFPRI, Middlebury College, Monash Development Workshop, Oregon State University, Otago Development Workshop, Otago International Health Research Network, Labor Econometrics Workshop, PopPov Annual Research Conference, University of Maryland, University of Oklahoma, University of Oregon, University of Southern California, University of California, Berkeley, Washington Area Development Economics Symposium, Yale University, and the World Bank. We thank everyone who provided this project with great fieldwork and research assistance and are too numerous to list individually. We acknowledge funding from the Global Development Network, the Bill and Melinda Gates Foundation, 3ie, NBER Africa Project, as well as the Research Support Budget and several trust funds from the World Bank – including the Knowledge for Change Program. Ethical review committees at the National Health Sciences Research Council (Malawi, Protocol #569) the University of California at San Diego (USA, Protocol #090378), and George Washington University (USA, Protocol #061037) approved the study design. The trial is registered at AEA RCT Registry (#AEARCTR-0000036).
<!-- Start of picture text --> 60 60 SA Peers<br>*° ————— “<br>20 MCcc 20 South America<br>0 0<br>Tegsssessnuansa ”gsssesegenaaz\ =<br>ol el cr Sel<br><!-- End of picture text --> <!-- Start of picture text --> 0.0<br>-0.5<br>-0.7<br>-1.0 09 0.9<br>-1.5<br>-15<br>2.0<br>Mcc MCC Peers South America SA Peers<br><!-- End of picture text -->
_Table 4.4 Marginal value of education by level along the conditional earnings distribution, Mexico 1988-_

||||_2_<br>**1988**|_002_||||**1992**|||
|---|---|---|---|---|---|---|---|---|---|---|
|**Quantile**|**0.1**<br>|**0.25**|**0.5**<br>|**0.75**|**0.9**|**0.1**<br>|**0.25**|**0.5**|**0.75**|**0.9**|
|_Primary Complete_|1.11|1.09|1.06|0.95|0.85|1.02|1.01|0.95|<br>0.81|<br>0.67|
|_Lower Secondary Complete_|1.21|1.18|1.19|1.24|1.27|1.15|1.18|1.21|<br>1.25|<br>1.3|
|_Upper Secondary Complete_|1.11|1.18|1.24|1.25|1.37|1.17|1.2|1.25|<br>1.32|<br>1.38|
|_Tertiary_|1.43|1.5|1.52|1.47|1.34|1.61|1.68|1.75|1.7|1.6|
||||**1997**|||||**2002**|||
|**Quantile**|**0.1**<br>|**0.25**|**0.5**<br>|**0.75**|**0.9**|**0.1**<br>|**0.25**|**0.5**|**0.75**|**0.9**|
|_Primary Complete_|1.12|1.13|1.13|1.11|1.05|1.14|1.1|1.1|<br>1.08|<br>1.04|
|_Lower Secondary Complete_|1.19|1.21|1.26|1.32|1.39|1.15|1.15|1.16|<br>1.21|<br>1.25|
|_Upper Secondary Complete_|1.15|1.22|1.27|1.35|1.42|1.13|1.15|1.21|<br>1.28|<br>1.34|
|_Tertiary_|1.75|1.91|1.95|1.83|1.67|1.67|1.82|1.91|<br>1.87|<br>1.73|

_Note_ : The marginal value is with respect to the previous education level. The asymptotic covariance matrix of the estimated coefficient vector in quantile regression is computed using the bootstrap method. All the coefficients are statistically significant at 5 per cent level and conditioned to age, squared age, gender, region (North, Center, South, and Mexico City).

_Source_ : WB Staff estimates using third quarter of ENEU 1997 to third quarter and urban section of ENET 2002.

29
the actual costs ended up being several times the original estimate." As a result, the research component of the project was dropped and large cutbacks were made in technical support services.

In 1976, with construction of the new factories still taking place, the Tanzanian government requested a second phase to the World Bank project. Concern was expressed about the lack of operational experience in the earlier funded factories and about the overall economic viability of additional factories. At the time, there was also some evidence of a decline in cashew nut production, although greater reliance was given to CATA's optomistic projections for future production levels. In any case, the second phase project was approved, involving the construction of three more factories with an additional capacity of 26,000 tons. Parallel bilateral financing for two additional factories would bring the total processing capacity to 113,000 tons by 1980.

Before any of the new factories came on line, Tanzania's cashew production began to plummet. From the 1973/74 peak of over 145,000 tons, marketed production fell quickly to 84,000 tons in 1975/76 and then declined through the late 1970s and early 1980s to reach only 31,100 tons in 1982/83 (Graph 4). Thus, just as the new factories were being completed, total national production was only one-third to one-fourth of factory capacity.

Graph 4: Marketed Cashew Nut Production in Tanzania (1974/75 - 1984/85) <!-- Start of picture text --> 120<br>100_ A<br>i20 eco- -<br>So-<br>70 -<br>so X<br>40 -<br>197A/75 | '77| 1976/79 | 198d/81 | 1996/83 1G84/85<br>197 /78 1971/78 1979/80 1981/82 1983/84<br><!-- End of picture text -->

1s Actual costs were 3.6 times original estimates.

15
# 2 Background and Data

## 2.1 Malaria Characteristics

Malaria is an infectious disease that requires two hostshumans and mosquitoesin order to spread. The malarial cycle for P. falciparum, the parasite causing 100 percent of cases in Senegal, can take several weeks (World Malaria Report 2014). After an infected individual is bit by a mosquito, there is an incubation period lasting around 9 days within the mosquito (Killeen, A. Ross, and T. Smith 2006).<sup>6</sup> If the mosquito survives the incubation period, it can bite and infect a healthy individual, after which there is a second incubation period within the human of around 15 days (D. L. Smith and McKenzie 2004, Hoshen and Morse 2004). Symptoms will appear at the end of this period and the individual will become infectious.<sup>7</sup> Combining the two incubation periods, a secondary case will take around one month to appear after a primary case.<sup>8</sup> This paper focuses on the role of human behavior on spread of the disease.<sup>9</sup> There are two channels through which population movement can lead to spread of malaria in lowmalaria or elimination zones. The rst is residents of these zones who travel to high malaria areas and become infected when bit by infected mosquitoes. Since malaria symptoms do not appear for around two weeks, the resident can travel home feeling healthy. Once at the home location, the person can become symptomatic, as well as infect mosquitoes. These infected mosquitoes can infect other individuals and pass on the disease. The second channel is visitors or migrants that live in a high malaria area and travel to a low malaria area. Again, at the beginning of their travel, these individuals might not exhibit symptoms, but can still be carriers of the disease.Therefore if they are bit by a mosquito in the low malaria area, they could infect that mosquito and it could in turn infect other individuals.

> 6The incubation period can vary, but two dierent sites in Senegal had an average of 9 days.

> 7Unlike other malarial parasites, P. falciparum does not have the potential to lie dormant for months.

> 8Details on malaria transmission can be found in Doolan, Dobaño, and Baird 2009, D. L. Smith and McKenzie 2004, Killeen, A. Ross, and T. Smith 2006, Wiser 2010.

> 9Average radius of travel for the mosquitoes that carry the malaria parasite in Senegal is only 1-2 km; therefore, mosquito movement is not considered (Russell and Santiago 1934, Thomas, Cross, and Bøgh 2013).

6
in motorway PPPs: A comparative policy analysis of alternative approaches,” _Journal of comparative policy analysis: Research and Practice_ , 17, 481–501.

- An, H. (2017): “Report: Selected Good Practices for Risk Allocation and Mitigation in Infrastructure in APEC Economies,” _Asia-Pacific Economic Cooperation_ .

- Anderson, M. B., M. Petrie, M. M. Alier, M. M. Cangiano, and M. R. Hemming (2006): _Publicprivate partnerships, government guarantees, and fiscal risk_ , International Monetary Fund.

- Arrow, K. J. and R. Lind (1970): “Uncertainty and the Evaluation of Public Investment Decisions,” _The American economic review_ .

- Aye, G. C., I. A. Adediran, and G. O. Odularu (2018): “Oil Price Shocks, Exchange Rate Volatility and Investment in Nigeria,” _AAU Annals of Accounting, Educational and Social Research_ , 2, 44–53.

- Christiansen, H. (2008): “The OECD principles for private sector participation in infrastructure,” _Public Investment and Public-Private Partnerships: Addressing Infrastructure Challenges and Managing Fiscal Risks_ , 143–161.

- Engel, E., M. Ferrari, R. Fischer, and A. Galetovic (2022a): “Managing the Fiscal Risks Wrought by PPPs: A Simple Framework and Some Lessons from Chile,” World Bank Policy Research Working Paper.

- Engel, E., R. Fischer, and A. Galetovic (2014): “Risk and public-private partnerships,” _CESifo DICE Report_ , 12, 3–7.

- ——— (2022b): “Private Finance of Public Infrastructure,” _Annual Review of Financial Economics_ , 14, 319–335.

- Engel, E., R. Fischer, A. Galetovic, J. Soto, et al. (2019): “Financing PPP projects with PVR contracts: Theory and evidence from the UK and Chile,” Tech. rep., University of Chile.

- Engel, E., R. D. Fischer, and A. Galetovic (2021): “When and How to Use Public-Private Partnerships in Infrastructure,” _Economic Analysis and Infrastructure Investment_ , 333.

- Engel, E. M., R. D. Fischer, and A. Galetovic (2001): “Least-present-value-of-revenue auctions and highway franchising,” _Journal of Political Economy_ , 109, 993–1020.

- Fay, M., L. A. Andres, C. Fox, U. Narloch, and M. Slawson (2017): _Rethinking infrastructure in Latin America and the Caribbean: Spending better to achieve more_ , World Bank Publications.

- Fay, M., D. Martimort, and S. Straub (2021): “Funding and financing infrastructure: the joint-use of public and private finance,” _Journal of Development Economics_ , 150, 102629.

- Fong, W. M., S. K. Koh, and S. Ouliaris (1997): “Joint variance-ratio tests of the martingale hypothesis for exchange rates,” _Journal of Business & Economic Statistics_ , 15, 51–59.

- Forum, W. E. (2016): “Risk Mitigation Instruments in Infrastructure: Gap Assessment,” _World Economic Forum_ .

49
Policy Research Working Paper 8352

# **Abstract**

Does decentralizing the allocation of public resources reduce rent-seeking and improve equity? This paper studies a governance reform in Pakistan’s vast Indus Basin irrigation system. Using canal discharge measurements across all of Punjab province, the analysis finds that water theft increased on channels taken over by local farmer organizations compared with channels that remained bureaucratically managed, leading to substantial wealth redistribution. The increase in water theft was greater along channels with larger landowners situated upstream. These findings are consistent with a model in which decentralization accentuates the political power of local elites by shifting the arena in which water rights are contested.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hjacoby@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
and economic data). An appealing feature of the APC ME method is that it overcomes the potential arbitrariness of identification restrictions.<sup>5</sup> We limit our analysis to workers ages 20–65.

# **5.2 Analysis of long-term trends in job tenure**

The APC decomposition focuses on the evolution of job tenure over time and age cohorts. But the characteristics of the members of cohorts could change over time. For example, individuals within a group may change their family or fertility status, type of employment, occupation, industry, and/or mobility preferences. If such characteristics affect job tenure, compositional changes will partly explain the cohort effects estimated by the APC approach.

This section discusses our empirical strategy for an individual-level analysis of the evolution of job tenure conditional on workers’ and employers’ characteristics that can change over time. The primary objective of this analysis is to understand whether changes in individual and employment characteristics of the labor force might explain long-term trends in job tenure. The persistence of a trend in tenure after controlling for these characteristics would imply that other factors, probably operating at the economy-wide level, may drive the evolution of job tenure.

We use a binary probit model to estimate equation (2) separately for the three durations of tenure.<sup>7</sup> Consistent with previous studies, we estimate the probability of being on the job for less than 1

> 5 The least squares estimator proposed by <u>Deaton and Paxson (1994)</u> remains popular and widely used in economic analysis, despite its deficiencies.

> 6 <u>Bratberg, Salvanes, and Vaage (2010)</u> use a similar methodology to analyze changes in job stability in Norway.

> 7 The duration of tenure is derived differently in the data from the countries in our sample. The majority of surveys asked a question about the number of years in the current job (as discussed in the Data section). The answers to such questions are subject to bunching and recall errors that could be correlated with the duration of tenure and thus bias our estimations (Duncan and Hill 1985, <u>Bollinger et al. 2019).</u> However, typically these errors could lead to a slight overestimation of the longer-term tenure, which, qualitatively, should not affect the main conclusions of our analysis.

11
where _xi_ is the vector of explanatory variables, and _ui_ is the error term, assumed to follow a standard normal distribution with mean zero and variance 1. In practice we only observe whether an individual works in the non-farm sector or not, we call _y_ the variable which indicates whether individuals participate in non-farm wage employment or not (or whether they participate in non-farm self-employment or not).

The probability that an individual participates is: Prob( _yi_ = 1) = 1 – F(-β _’xi_ ) where F is the cumulative Normal distribution function.

Based on the analysis of previous empirical and theoretical studies (including Woldenhanna and Oskam, (2001); Reardon (1997); Lanjouw et al. (2001)) and in the context of Rwanda, there are four sets of explanatory variables.

The first set includes _individual characteristics._ These are the following: educational attainment, age, gender, migration history, parents’ education, parents’ occupation, presence of parents in household, and household member status. These characteristics constitute the personal assets of individuals; they influence the quality of jobs that they can or expect to get. The quality of a job is determined primarily by the wage and responsibilities offered and therefore the social status of individuals. Specifically, educational attainment, age and migration history represent the skills that individuals have gained. Parental variables indicate both inherited ability and social networks. Household member status gives an indication of the role of individuals in their household, in particular whether they may be considered as the main breadwinner.

The second set includes _household level characteristics_ : number of dependents (14 and younger) divided by total number of household members, land holdings, and livestock. The latter two variables are measured one year before the interview. The former two variables are linked to the family responsibilities of individuals; for example, in two-adult households, both adults can participate in the labor market without facing additional constraints due to having to care for other family members. In Rwanda, mono-parental, female-headed households represent a significant proportion of households and a group “at risk” of greater poverty.

We include a set of _dummy variables for the province_ . In the context of Rwanda, provinces have been affected differently from the various waves of refugees and migrants. We therefore account for that and potentially other provincial characteristics.

Finally we control for _community level variables_ . We do not have any direct information regarding the social capital available in the community, such as that suggested in Lanjouw et al. (2001). We only have what we consider to be indirect evidence of this: net 8
|Table S.9|: Impacts|on children’|s educatio|nal achievem|ent - Gen|eral scien|ce: heteroge|neity (O|LS)|
|---|---|---|---|---|---|---|---|---|---|
|Dependent variable:||||General scie|nce: GPA z|-scores||||
|_X_:|Age|Education|Male|Household<br>size|No. of<br>male<br>members|Head<br>age|Head<br>education|Head<br>male|Land<br>size|
||(1a)|(1b)|(1c)|(1d)|(1e)|(1f)|(1g)|(1h)|(1i)|
|**December in 2013**||||||||||
|All products|-0.428|0.156|-0.009|0.234|0.206|0.383|0.067|0.068|0.029|
||(0.675)|(0.305)|(0.095)|(0.263)|(0.172)|(0.414)|(0.086)|(0.224)|(0.084)|
|One product|0.675|0.259|0.017|0.003|0.007|-0.049|0.025|0.056|0.029|
||(0.675)|(0.283)|(0.091)|(0.296)|(0.161)|(0.363)|(0.075)|(0.204)|(0.083)|
|All products _× X_|0.039|-0.023|0.144|-0.037|-0.064|-0.008|-0.012|-0.018|0.002|
||(0.052)|(0.058)|(0.126)|(0.048)|(0.056)|(0.010)|(0.034)|(0.234)|(0.002)|
|One product _× X_|-0.051|-0.048|0.042|0.007|0.014|0.002|0.011|-0.021|0.001|
||(0.053)|(0.055)|(0.151)|(0.057)|(0.063)|(0.008)|(0.035)|(0.206)|(0.003)|
|R-squared|0.299|0.298|0.298|0.298|0.298|0.298|0.297|0.297|0.298|
|No. of obs.|850|850|850|850|850|850|850|850|850|
||(2a)|(2b)|(2c)|(2d)|(2e)|(2f)|(2g)|(2h)|(2i)|
|**December in 2014**||||||||||
|All products|-1.166**|-0.303|-0.041|-0.558|-0.080|0.318|-0.132|-0.411|-0.115|
||(0.455)|(0.259)|(0.096)|(0.353)|(0.178)|(0.309)|(0.093)|(0.323)|(0.085)|
|One product|-0.566|0.083|-0.077|-0.084|-0.035|0.076|-0.077|-0.458*|-0.059|
||(0.671)|(0.305)|(0.116)|(0.346)|(0.207)|(0.463)|(0.112)|(0.254)|(0.104)|
|All products _× X_|0.086**|0.044|-0.143|0.092|-0.009|-0.010|0.023|0.341|0.001|
||(0.039)|(0.056)|(0.173)|(0.066)|(0.068)|(0.007)|(0.033)|(0.313)|(0.002)|
|One product _× X_|0.041|-0.031|0.037|0.004|-0.012|-0.003|0.010|0.432|-0.000|
||(0.053)|(0.059)|(0.175)|(0.069)|(0.085)|(0.011)|(0.035)|(0.280)|(0.003)|
|R-squared|0.151|0.150|0.149|0.151|0.148|0.149|0.149|0.151|0.148|
|No. of obs.|846|846|846|846|846|846|846|846|846|
|Baseline control|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

54
US Treasury has halted its buyback program, as the weakened US economy, the Bush Administration tax cuts, and significant expenditures relating to the war on terrorism, homeland security and the war in Iraq have increased the spending needs of the US government.<sup>28</sup> US Treasury securities are direct obligations, and are backed by the full faith and credit, of the US government. Given the sound financial history of the US government, and its ability to generate tax revenues, US Treasury securities are considered to be virtually risk free. Because of their uniquely sound and stable status, US Treasury securities play a key role in debt markets worldwide. They are widely used as a benchmark for pricing other fixed-income securities,<sup>29</sup> and also are extensively used for investing and hedging purposes. Interest on US Treasury securities is free from state and local taxes, although it is subject to US federal income tax, and the securities themselves are exempt from registration with the SEC.<sup>30</sup> 2. _GSEs._ Fannie Mae, Freddie Mac and Ginnie Mae were all created by federal law to promote a secondary market for residential mortgage loans and to enhance their liquidity. The existence of the secondary market for mortgage loans enables the originating lenders to sell their loans and use the proceeds to offer new mortgage loans. Fannie Mae was created in 1938 as part of the Federal Housing Administration. In 1968, it was split into two entities, one of which became the investor-owned company that exists today, the other of which, Ginnie Mae, became a wholly owned government corporation. Congress created Freddie Mac in 1970, and it became an investor-owned company in 1989. Fannie Mae and Freddie Mac guarantee payments on their respective securities. Ginnie Mae does not itself issue securities, but guarantees mortgage-backed securities that are issued by qualified mortgage lenders. The common stock of both Fannie Mae and Freddie Mac is listed on the New York Stock Exchange.

The market in mortgage-backed securities began to grow substantially in the 1980s. While the investor base has evolved, it still remains a largely institutional market. Indeed, the GSEs themselves hold more mortgage-backed securities than any other individual investor in the market. Among other things, GSE securities also are widely used for hedging purposes, including hedging “pipeline risk” in the “to-be announced” GSE market.<sup>31</sup>

> 28 In 1971, Congress set the U.S. Treasury's debt ceiling “permanently” at $400 Billion.  However, socalled “temporary” increases in the debt ceiling continued.  In 1982, actual Treasury debt was three times the “permanent” ceiling, so Congress eliminated the distinction between "temporary" and "permanent" Most recently, Congress raised the debt ceiling from $6.4 trillion to $7.4 trillion on May 27, 2003. 29 IMF, 1998.

> 30 Section 3(a)2 of the Securities Act; Section 3(a)(12)Ai of the Exchange Act.

> 31 “Pipeline risk” is the risk that not all commitments for underlying mortgage loans will close.  US SEC, January 2003.

11