rafmacalaba/data-use-mentions-extended huggingface dataset:
Because we vary the proposed consumption charge across the sample of households, the estimated coefficient on this variable allows us to estimate average WTP (Hanemann, 1984; Cameron, 1988; Cameron and James, 1987). The coefficients on the remaining variables essentially allow us to estimate how this WTP and the response varies by household characteristics. By including household characteristics, we can estimate the distribution of WTP by sub‐groups such as regions, socio‐economic status, and water use type. We also investigate differences between these sub‐groups by using interaction terms. The results in Section 4.1 illustrate each of these uses of WTP data, including this first attempt to account for population heterogeneity.

Recall our motivation for an alternative operational use of the response to WTP surveys, relying on very simple logic. The use of a split sample survey design in which the type of provider in the WTP scenario is varied across the sample of households, the response data traces out a sample demand function for the improved piped water service. That is, for each proposed scenario we can derive the portion of the sample (or number of households) who will sign up (or are willing to pay) for the improved service (see Van Houtven et al. 2000; Cahn et al. 2004 for applications in the health field). We also know that the respondents come from different backgrounds and face different constraints. Moreover, some of these constraints can be relaxed through policy interventions such as subsidies (for use or connection) and information and education. When this data is used in a regression model, we can determine how this estimated sample demand curve “will shift” if those household characteristic and constraints change. This is the gist of the logic used to understand WTP data and employ it to simulate coverage rates of piped water connections for different groups and under different policy scenarios. Section 4.2 illustrates these uses of the WTP data.

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|**Table 4. R**|**esolving the Russian P**|**aradox**||
|---|---|---|---|
|Model|A|B|C|
|_Estimation Method_|_b_|_etween panel estimat_|_or _|
|_Dependent variable_|_de_|_gree of decentralizati_|_on_|
|Real GRP per capita<br>log,PPPadjusted|-0.192<br>(0.043)***||-0.166<br>(0.054)***|
|Real GRP per capita<br>log||-0.215<br>(0.062)***||
|Fuel industry share|-0.0022<br>(0.0008)**|-0.0020<br>(0.0011)*|-0.0026<br>(0.0009)***|
|Excess budget transfer revenue|-0.662|-0.118|-0.639|
||(0.242)***|(0.276)|(0.256)**|
|Social transfers|0.018|0.006|0.020|
|as share of incomein 1995|(0.013)|(0.018)|(0.014)|
|Infant mortality|0.008<br>(0.004)**||0.0057<br>(0.0040)|
|Infant mortality in 1990||-0.00007<br>(0.00065)||
|Gini coefficient||0.189|-0.007|
|(population in 2001)||(0.410)|(0.363)|
|Gini coefficient||0.083||
take a pragmatic approach focusing on the context of the medical profession and rural posting. In this context we expect the motivation to help the poor – equivalent to healing the most severely ill patients since health and income poverty are strongly related - to play an important role.<sup>20</sup> The variable we use is based on a pre-coded survey question about the relative importance of different job attributes. All listed attributes are at least partially driven by extrinsic motivation (career concerns, salary, etc.), with the exception of ‘opportunity to help the poor’. The intrinsic motivation variable is then constructed as a dummy variable indicating that ‘opportunity to help the poor’ is ranked highest. Because our analysis indicates that this variable is an important determinant of willingness to work in a rural area, we also look at what determines an individual’s willingness to serve the poor and endeavor to explore the extent to which it might be possible to influence such motivations.

> 19 The validity of the reservation wages obtained from contingent valuation is supported by two additional results.  To capture the long term preference and expectation of the students with respect to urban or rural posting, we asked two more questions: whether the long term preference is to work in an urban or rural area; and what they think their employment situation will be in five years. The answers to both questions are highly corelated with the reservation wages obtained from contingent valuation with as correlation coefficients  -0.43 and -0.83 respectively.

> 20 Agreement on the importance of this norm is of course best reflected in the Hyppocratic Oath: ‘treat the sick to the best of one’s ability’.

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which institutional and other obstacles to trade in this region deviate from expected values based on income, population and geography.

The composite indicators are drawn from the World Economic Forum (WEF) Enabling Trade Index (ETI), the World Bank Logistics Performance Index (LPI) and an index constructed from the World Bank Trading on Time variables presented in Table 7.<sup>14</sup> Higher values reflect fewer obstacles to international trade.

# **Table** **<u>8: Determinants of Trade Cost indicators</u>**

||ln<br>(GDP/Capita)|Ln<br>(GDP/Capita)<sup>2</sup>|Ln(area)|ln(population)|Landlocked|SADC<br>dummy|N|R<sup>2</sup>|
|---|---|---|---|---|---|---|---|---|
|Tdi<br>|<br>Overall index<br>-0.168|0.028|-0.265***|0.117***|-0.918***|-0.085|187|0.605|
|rang<br>on<br>|<br>Import index<br>0.032|0.017|-0.245***|0.103**|-0.990***|-0.149|187|0.603|
|Time index|<br>Export index<br>-0.368|0.039*|-0.286***|0.131***|-0.847***|-0.022|187|0.573|
||Overall index<br
capabilities. Finally, it studies for the first time the relationship between firm capabilities and performance in Mozambique.

We find that larger firms, male-owned, with relationship with international markets (exporters, buying inputs from outside, with licensed technology), and those using internet, are more likely to have better firm capabilities. Measure of competition, captured by Herfindahl-Hirschman Index (HHI), however, does not have significant effect on firm capability in Mozambique, contrary to

> 1 On average, informal firms in Mozambique are 14 times less productive than micro formal firms. See also Aga et al. 2019.

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> ; and the World Bank’s measure of creditors’ protection index. The later comes from the World Bank’s Doing Business database. It is an indicator of creditor rights in insolvency, based on the methodology of La Porta and others (1998). The indicator measures four powers of secured lenders in liquidation and reorganization, such as priority access to the proceeds from liquidation. The aggregate creditor rights index sums the total score across all four variables. A minimum score of 0 represents weak creditor rights and the maximum score of four represents strong creditor rights.

Each of those variables has been re-scaled so the best is assigned the number 10 and the worst 0. The overall access to finance index is a simple average of all components.

# **_Exit costs_**

The World Bank’s database “Doing Businesses” is one of the first attempts to measure in a comparable way the exit costs. Members of the International Bar Association's Committee on Insolvency and participating law firms or bankruptcy judges from around the world were sent a questionnaire enquiring about the number of procedures, time and cost of the process, as well as about the preservation of the priority rule to secured creditors and the efficiency of the outcome of the bankruptcy process. The index captures the summary indicator _“Goals of insolvency index”_ which measures whether the insolvency law achieves its goals successfully. The higher the index, the more successful is the system.

> 13 James Gwartney, Robert Lawson and Neil Emerick: Economic Freedom of the World Annual reports. The Fraser Institute, Canada.

> 14 See appendix 1 for country grouping

> 15 Garcia, Gillian (1999): „Deposit insurance: a survey of actual and best practices“ IMF WP/99/54

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20

# **5.2.1. Schooling and Employment Shocks**

The size of the schooling shock amounts to an estimate of the value of the parameter _ν_ in the education shock equation: _hit_ = _hi,t−_ 1 + (1 _− ν_ ) _rs_ ( _sit_ ). For each group, we obtain _ν_ in the following manner. First, we obtain estimates of the school closure rate _s_ for each year (1, 2). Students at schools that were closed or partially closed continued their education in some form or another – through the internet, via TV or the radio, or by receiving work packs at home – with relative reach _c_ and effectiveness _e_ . Thus, the share 1 _−s_ children attend school as normal, and the share _s_ of children participate in distance learning with reach _c_ and effectiveness _e_ . Finally, we set _ν_ = _s ×_ (1 _− ce_ ). **School closures** UNESCO (2021) provides data on daily school closures globally. For each country and school day, we know the share of schools that are _fully open_ , _partially open_ or _closed_ . The data cover the period 02-16-2020 until 10-31-2021. For days that schools are supposed to be open but are _closed_ , we assume online instruction. For days that schools are _partially open_ , we assume a hybrid system where half the students are present and half are online (see Web Appx. Section C for details on our assumptions).

A school year lasts 9.5 months on average globally. We thus examine the share of _not fully open_ school days (using the formula “ _closed_ + 0.5 _partially open_ ”) for the first 9 _._ 5 school months (starting 02-16-2020) against the last 5-8 school months (which depends on the country’s school year schedule). We think of the last 5-8 school months as being representative of the second year of disruption. This provides estimates of _s_ .<sup>
this phenomenon. They show the difference in yields (SR-CC) across quintiles of plot area (measured via GPS) for the entire sample. These figures highlight a consistent correlation between the SR-CC difference and the quantity of cultivated area, as noted in the literature: farmers’ overestimation is more pronounced on smaller plots and decreases, or even reverses, on larger plots.

The average overestimation of SR yields in comparison to CC yields in Mali is likely influenced by the fact that the average plot area in Mali is larger than in the studies previously mentioned. This larger average plot size means that the underestimation trend observed in other studies begins earlier in the distribution for Mali. Generally, SR yields are overestimated compared to CC yields in the first plot area quintile. However, in each of the subsequent quintiles, SR yields tend to be underestimated. The distinct trend observed for cowpea may be attributed to the fact that cowpeas are typically planted on smaller plots. Despite this, the general trend of decreasing overestimation across land quintiles is still evident, though it is not a linear decrease.

Additionally, the high confidence interval reported for cowpea yields may be attributed to the prevalence of intercropping in its cultivation.<sup>7</sup> Accurately self-reporting harvest quantities in intercropped plots can be particularly challenging. As for groundnut, we observe consistent underestimation across all plots. This finding could be linked to the findings by (Bardasi et al., 2011), which suggests that women’s labor tends to be underestimated by men. A plausible explanation for the underestimation observed in our study is that groundnuts are predominantly cultivated by women, while the majority of respondents for the 2017 and 2018 surveys were men, who usually tend to underreport the product harvested by women. According to EACI-17 and EAC-18 data, groundnut plots, which are managed by women at a higher rate than average (18% compared to the overall average of 7%), also have a high rate of proxy respondents. Specifically, 42% of the
Policy Research Working Paper 7705

# **Abstract**

Rwanda’s completion, in 2012/13, of a land tenure regularization program covering the entire country allows the use of administrative data to describe initial performance and combine the data with household surveys to quantify to what extent and why subsequent transfers remain informal, and how to address this. In 2014/15, annual volumes of registered sales ranged between 5.6 percent for residential land in Kigali and 0.1 percent for agricultural land in the rest of the country; and US$2.6 billion worth of mortgages were secured against land and property. Yet, informality of transfers in rural areas remains high. Decentralized service provision and information campaigns help reduce but not eliminate the extent of informality. A strategy to test the efficacy of different approaches to ensure full registration, scale up promising ones, and rigorously monitor the effect of doing so is described.

This paper is a product of the Agriculture and Rural Development 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 dali1@worldbank.org, kdeininger@worldbank.org, and mduponchel@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
encompassing not only infants and young children but also older children and adolescents, thereby providing a fuller picture of the developmental trajectories of forcibly displaced youth.

Finally, our work contributes to the body of research examining the impact of children’s experiences on cognitive and socio-emotional development, particularly in developing countries. Pioneering research has shed light on the developmental deficits experienced by children living in poverty, emphasizing the profound and lasting effects these deficits have on the intergenerational transmission of poverty (Attanasio, Cattan and Meghir 2022, Alesina et al. 2021). By focusing on the specific context of forced displacement, our work adds a crucial dimension to this existing literature, exploring how such displacement interacts with the human development process in the short term. In fact, given that over 75% of the world’s are hosted in coun- forcibly displaced populations developing tries (UNHCR 2023), understanding the nuances of how forced migration affects child development is both pertinent and urgent. Our analysis of the immediate developmental impacts of forced displacement provides valuable insights into a phenomenon that is both widespread and deeply consequential in the developing world, highlighting the critical need for policies and interventions that address the unique challenges faced by displaced children and adolescents.

The remainder of the paper is organized into five main sections. The upcoming section provides an overview of the situation facing Venezuelan migrants in Colombia, setting the stage for understanding the context within which our study is situated. Section three offers a comprehensive description of the VenRePS-Kids study, covering aspects such as the sampling frame, the instrument used for data collection, the representativeness of the study, its implementation process, and an overview of descriptive statistics. Section four delves into the human development disparities observed among forcibly displaced children and adolescents, providing detailed insights into the nature of these gaps. In section 9
They use two nationally representative surveys in Malawi conducted in the same year but following different research protocols. They find underreporting of certain labor activities in the survey using “business as usual,” that allowed for proxy reporting and presence of other household members, relative to the other survey that required own response and privacy during the interview. Contrasting their setup, with multiple differences between the “business as usual” and the more stringent protocols, our study uses experimental variation designed to isolate a single mechanism, recall windows. Our heterogeneous results, showing greater long recall loss for

> 4 Das and Sanchez-Paramo (2012) find that one-third of acute illness is unreported when comparing one month to one week recall periods. Other notable examples showing high sensitivity of consumption to recall windows include Beegle et al. (2012b), De Weerdt et al. (2016), Backiny-Yetna et al. (2017), and Di Maio and Fiala (2019). Beegle et al. (2012a) find little evidence of distortions from longer recall lags in recording agricultural inputs and production.
Another explanation of the difference in political cycles could arise from comparing national cycles to sub-national cycles. The literature thus far has focussed on the effect of presidential and national parliamentary elections, where it may be easier to manipulate fiscal and monetary policy to affect voter perceptions. However, at sub-national levels, voters may evaluate candidates on more micro indicators of performance, and a moral hazard model of government accountability may be more appropriate. Further research on the contrast of election strategies and voting behavior in national versus local elections would provide insights to the question of political accountability at different levels of government.

# References

- [1] Alesina, A. [1987], "Macroeconomic Policy in a Two-Party System as a Repeated Game", Quarterly Journal of Economics, 102, 651-678.

- [2] Alesina, A. and Roubini, N. [19921, "Political Cycles in OECD Economies", Review of Economic Studies, 59, 663-688.

- [3] Alesina, A., Roubini, N., and Cohen, G. D. [1997], Political Cycles and the Macroeconomy, MIT Press.

- [4] Basic Roads Statistics of India, Ministry of Surface Transport, Government of India, 1960-1995.

- [5] Bardhan, P. and Mookherjee, D. [1999] "Relative Capture of Local and Central Governments", Working paper.

- [6] Besley, T. and Case, A. [1995], "Does Electoral Accountability Affect Economic Policy Choices? Evidence from Gubernatorial Term Limits", Quarterly Journal of Economics, 110[3], 769-798.

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birth. (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1) Sample corresponds to children born between 1957 and 1962, or 1968 to 1972. Covariates include birth district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). Primary completion takes the value of 1 if the child has completed primary and 0, otherwise. Father’s Edu represents father’s years of schooling, which was calculated based on the education level completed. Family background is measured by father’s years of schooling. The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

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1.5% of the total _rabi_ surplus generated by the borewell, similar in magnitude to the average fixed transaction cost of 1.8%. For the same reason as before, the contracting distortion is greater for borewell owners with smaller plots. Interestingly, comparing the right and left panels of the figure, the impact of groundwater market frictions on area irrigated is, in percentage terms, roughly triple its impact on welfare.

Finally, shutting down uncertainty altogether by means of counterfactual scenario (3) results in modest gains in both welfare (in this case, more so for larger plots) _and_ in area irrigated.<sup>34</sup> Thus, in reference to the reduced form regressions in Table 2, our model suggests that groundwater market distortions are far more important than the precautionary planting motive in explaining the variation in irrigated area due to groundwater supply uncertainty.

# **6.2 Drip irrigation and land fragmentation**

Based on agricultural experiments in India, water use efficiency is 2-3 times greater under drip than under flood irrigation (Narayanamoorthy 2006). Taking the conservative estimate of a doubling of irrigation efficiency, we use the model, through a doubling of the effective discharge parameter _λ_ , to simulate the gross return to drip adoption<sup>35</sup> Based on the four-district estimation sample, the average borewell owner would realize a 53% surplus gain by converting his plot to drip irrigation, with an interquartile range of 44-65%.

The interesting question, however, is how these gains are achieved, since the gross return reflects the full range of endogenous responses to increased irrigation efficiency, including more intensive cultivation of the borewell plot, increased water sales, and switches between alternative transfer arrangements. To gauge the nature and extent of these responses, Table 8 reports the predicted transition probabilites pre- to post-drip adoption. Thus, we see that of those starting in unconstrained self cultivation ( _U_ ), 62% remain unconstrained postdrip, 13% move to autarky ( _A_
<!-- Start of picture text --> Figure 1<br>Cross-Country Relation between Education Attainment and GDP<br>12 -<br>10 :*.+# .<br>Mexico 1990<br>8<br>6<br>4<br>o<br>0<br>5.00 S.S0 6.00 6.50 7.00 7.50 8.00 8.50 9.00 9.50 10.00<br>Ln (GDP per capita)<br><!-- End of picture text --> Source: Penn **World Tables,** mark **5.6,** and Barro-Lee data set on international measures **of** schooling. **The** chart **is based** on stacked cross-country data for 1960, 1970, 1980, 1985 and 1990. Country coverage varies according to data availability. U.1-achler
and mother's education and employment affects girls the most In the model, we include the number of years of education of each parent, and an interaction variable with the gender of the child. The nature of parents' employment also matters-if the parents have no or irregular employment, it creates the need for additional income sources to be provided by children. Due to sample size limitations, the employment aspect in the model below is captured only by a categorical variable indicating whether the parent is employed as wage earner or self-employed (i.e. excluding unpaid family workers). This variable is also interacted with the gender of the child.<sup>4</sup> _Household characteristics._ Several demographic and economic features of the household as a unit affect the supply of child labor.' On the demographic side, household size and composition are of foremost importance. Ceteris paribus, the more children there are in the household, the more likely it is that one of them will work. The literature has clearly established that larger household size reduces children's educational participation and reduces parental investment in schooling (Lloyd, 1994). A larger household size decreases income per capita and increases the dependency ratio, and both factors increase the likelihood that a child will need to generate income (in cash or in kind) to maintain the household's level of living. However, each child does not have the

> 4 If the parent is not a member of the household, we selected the education and employment characteristics of the oldest male or female person in the household.

> _5_ Cultural household characteristics could also be relevant in the child labor decision, e.g. religion. This type of information is not available in the CILSS.

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