rafmacalaba/data-use-mentions-extended huggingface dataset:
based on a measure of distance from the United States, which the author argues is the primary source of most imports. In keeping with most of this literature, he finds incomplete pass-through of the tariff changes to consumers in Mexico, with the extent of pass-through being smaller for agriculture than for manufactured goods. When coupled with a rapid erosion of pass-through with increasing distance from the border, this means that agricultural tariff cuts have little or no impact on the more remote regions of Mexico. Nicita argues that this low pass-through for agricultural products is due to the fact that transportation costs are high, and these products face more competition from domestic sources so that local production quickly becomes more profitable as one moves away from the border. Indeed, he notes: “domestic supply is likely to set the price of certain agricultural products regardless of border measures” (p. 23). This type of assessment sharply alters our view of the impact of trade liberalization on rural households, and much more work on the problem of price transmission is needed.

**_Data Reconciliation:_** The proper treatment of marketing margins is also important when one seeks to reconcile household survey data with the national accounts upon which CGE models are based.  Expenditures in the survey are reported at consumer prices, while most CGE models measure consumer demand at producer prices. In order to bridge the two, one needs a producer-consumer transition matrix (e.g., Ballard et al., 1985).  Of the studies referred to above, only the one by Hertel et al. (2004b) explicitly models this wholesale/retail/transport margin.<sup>13</sup> In most studies, the two data sets are either allowed to diverge, or they are forced to balance by making arbitrary adjustments on both sides. This can give rise to misleading inferences about the incidence of commodity price changes on the poor, in particular, for the same reasons mentioned above.

Data reconciliation between household surveys and national accounts is also required
# **Table A10: Role of information and communication technologies (ICTs) as mediator**

||Poverty $1.90|Gini index|
|---|---|---|
||(1)|(2)|
|_Panel A: ICT Development index_|||
|Temperature* ICT Index|-0.178***<br>(0.031)|-0.003***<br>(0.001)|
|_Panel B: Internet 2G_|||
|Temperature*Internet coverage|-2.980***<br>(0.997)|-0.034***<br>(0.013)|
|_Panel C: Internet 3G_|||
|Temperature*Internet coverage|-1.594***<br>(0.428)|-0.012***<br>(0.004)|
|_Panel D: Internet 4G_|||
|Temperature*Internet coverage|-0.762**<br>(0.297)|-0.019***<br>(0.006)|
|Controlling for rainfall|Yes|Yes|
|Subnational FE|Yes|Yes|

_Notes:_ Results of panel fixed effects model. Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. *** p<0.01, ** p<0.05, * p<0.1 58
productivity gains.

Analysis of firm‐level data shows that Egypt has experienced substantial progress in labor and total factor productivity between 2004 and 2008 (World Bank 2009). Moreover, the same study reports substantial progress in improving the overall investment climate during these years. In effect, the country experienced significant improvements in the tax code, in customs and tax administration, and in how costly it is to open a new business—in monetary and non‐monetary terms. Yet, firms still report macroeconomic and regulatory uncertainty as 23
(2010). Climate change, agriculture, and poverty.</mark> _<mark>Applied Economic Perspectives and Policy</mark>_ <mark>,</mark> _<mark>32</mark>_ <mark>(3), 355-385.</mark>

- Hsiang, S. M. (2010). Temperatures and cyclones strongly associated with economic production in the Caribbean and Central America. _Proceedings of the National Academy of Sciences_ , _107_ (35), 15367-15372.

- Hsiang, S., Oliva, P., & Walker, R. (2019). The distribution of environmental damages. _Review of Environmental Economics and Policy, 13_ (1), 83-103.

- Iizumi, T., & Sakai, T. (2020). The global dataset of historical yields for major crops 1981– 2016. _Scientific Data_ , _7_ (1), 1–7.

- Intergovernmental Panel on Climate Change. (2018). _Global warming of 1.5° C: An IPCC special report on the impacts of global warming of 1.5° C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty_ . Intergovernmental Panel on Climate Change. In Masson-Delmotte _et al._ (eds.) Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−24.

- Intergovernmental Panel on Climate Change (IPCC). (2021). “Technical Summary.” In _Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change_ . In Masson-

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and international bonds declined by about 42 and 29 percent, respectively, between 2008-16 and 2000-07, at the same

> 16 An increase in demand (albeit slower than the increase in supply) could still have occurred (di Giovanni et al., 2019). 17 We do not collapse these regressions at the economy-industry level given the large number of controls. These regressions exclude China because of its large weight in the issuance data. But including China does not change the results.

17
In addition to the relationships referred to in the preceding, the Congo MAMS application includes an additional set of functions that link education indicators and MDG 4 (under-five mortality) to a set of determinants. The determinants include the delivery of relevant services (in education and health) and other indicators, also allowing for the presence of synergies between MDGs, i.e. the fact that achievements in terms of one MDG can have an impact on other MDGs. For health, service delivery is expressed relative to the size of the population. In education, the model tracks base-year stocks of students and new entrants through the different grades of the three levels (primary, secondary, and tertiary). In each year, students will successfully complete their grade, repeat it, or drop out of their level. Student performance (rates of intake to the 1<sup>st</sup> grade of each cycle among the qualified population and rates of promotion) depends on educational quality (quantity of services per student), household welfare (measured by per-capita household consumption), the level of “other infrastructure”, wage incentives (expressed as the ratio between the wages for labor at the next higher and current levels of education for the student in question; an indicator of payoff from continued education), and health status (proxied by MDG 4). A built-in poverty module assumes that the distribution of per-capita consumption follows a lognormal distribution parameterized on the basis of the observed Gini coefficient. This information and data on the national poverty rate (in 2008) is used to compute standard Foster-Green-Thorbecke poverty indicators.

19
19 identified in this data set reflect a series of endogenous rather than exogenous choices made by the household. However, the management and taste factors that affect such choices should be fixed, and, therefore should not seriously bias the regression estimates.

Following the standard household model, it can be assumed that a rural household maximizes utility by allocating the land, labor and capital of its family members to various agricultural and non-agricultural tasks. From the first-order optimum conditions, we can derive land, labor time and capital service allocation functions to various household tasks that commonly depend on a set of factor prices, technology, personal characteristics of household members, and ownership of land and nonland resources. Factor prices (including land rent and residual return to land) depend on technology and personal household characteristics (such as management ability) that cannot be assumed to be exogenous. For this reason, it is desirable to estimate the factor price and factor allocation functions simultaneously. Unfortunately, this procedure cannot be used here because the quantities and prices of household-supplied factors for most household activities cannot be accurately estimated, either in this or most other household-level surveys. Therefore, in this section we estimate the reduced form income determination functions without distinguishing factor prices and quantities, which depend on technology, ownership of resources, and other household characteristics.

Specifically, in order to identify the determinants of income, we regress each of the five sources of income in this study - nonfarm, agricultural, transfer, livestock and rental - on three types of household-level inputs which are thought to cause income: land (i.e. size of landowned, size of land rented in); labor (i.e. household size, number of males over age 15, education of males); and capital (i.e. value of livestock owned, value
mation on children’s participation in (pre)primary education facilities. Appendix A presents detailed information on the data and the underlying variables. We estimate Equation 2 as follows: We also control for additional interaction terms.<sup>7</sup> Table 4 presents the results. The estimate of _β_ 4 is not stable across model specifications. Estimates vary in magnitude and size. This pattern of results might be driven by the small sample size. We also observe that estimates are insignificant in all model specifications. Results are similar when abstracting from the additional interaction terms (see Online Appendix Table B9). Estimates are more stable when restricting the sample to 20-to-48-year-old mothers. The estimates presented in Online Appendix tables B10 and B11 remain positive across most model specifications, but are insignificant at the common significance levels. The difference between the results presented in Tables 4 and 2 could be due to diverging concepts of work between the underlying surveys. The EU-SILC captures an annual and more stable concept of work than the EULFS.

We conclude that estimates are less robust when employing a triple difference estimation. These results might be driven by the small sample size, or by unobservable confounding factors that impact mothers exposed to the reform and complying with it differently than mothers who are not exposed or who do not comply. While we rule out the existence of potential confounding shocks or policies later in this paper, we cannot rule out potentially significant differences in unobservable characteristics between the different groups of mothers.

> 7 We control for _Assistist × Post_ 2012 _ist, Post_ 2012 _ist × Mom_ 6 _ist_ , and _Assistist × Mom_ 6 _ist_ .

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gaps in productivity. Closely related is the literature that explore the barriers to movement of labor out of agriculture (Todaro (1969), Bryan _et al._ (2014)). Another strand studies imperfections in credit markets that affect non-Agricultural sectors disproportionately driven by acute need of finance (Caballero _et al._ (2008), Buera _et al._ (2011), Song _et al._ (2011)). Credit market imperfections thus create hurdles for Industry and Services and stunt their growth leading to a depressed demand for resources. This work is also related to a large literature that tries to explain low agricultural productivity in poor countries (Restuccia _et al._ (2008), Adamopoulos (2011), Adamopoulos & Restuccia (2014), Gollin & Rogerson (2014) etc.). Finally, it also relates to the literature that argues that the measured gaps in productivity across sectors can be in large part explained by accounting for differences in human capital across sectors (Lagakos & Waugh (2013), Young (2013), Herrendorf & Schoellman (2015)). The measurement of labor inputs in this paper abstracts from differences in hours worked, human capital and talent across sectors due to the unavailability of time series data. Given the

> 2The estimates of distortions for the most recent decade are available for 84 countries. There is a large variation in GDP per capita across this sample with the country at 90<sup>th</sup> percentile being 15 multiples richer than the country at 10<sup>th</sup> percentile.

3
# **Early Education, Preferences, and Decision-Making Abilities**

Joana Cardim (Education Policy Institute) Pedro Carneiro (University College of London, IFS, CEMMAP, FAIR-NHH) Leandro S. Carvalho (University of Southern California) Damien de Walque (Development Research Group, The World Bank)<sup>1</sup> JEL codes: J13, I20, D91, J16, D81 Keywords: economic preferences, decision-making abilities, early education, daycare, children 1 Cardim; Education Policy Institute, 150 Buckingham Palace Road, London SW1W 9TR, UK (joana.almeidacardim@gmail.com) Carneiro: University College London, Department of Economics, Gower Street, London WC1E 6BT, UK (p.carneiro@ucl.ac.uk); Carvalho: University of Southern California, Center for Economic and Social Research, 635 Downey Way, Los Angeles, CA 90089-3332 (leandro.carvalho@usc.edu); de Walque: Development Research Group, The World Bank, Washington, DC 20433 (ddewalque@worldbank.org). Jiewen Luo provided excellent research assistance. This paper benefited from discussions with Isabelle Brocas, Juan Carrillo, Marco Castillo, Gary Charness, Niree Kodaverdian, Ragan Petrie, and Anya Samek, Lise Vesterlund, and from the feedback of participants of the Experiments which Children and Non-Standard Subjects Workshop at the 2019 North American Meetings of the Economic Science Association, the Nordic Conference in Development Economics (Helsinki 2022), the International Conference on Development Economics (Clermont-Ferrand 2022) and the European Economic Association Congress (Milan 2022). We thank Elisabet Meireles, Luiz Eduardo Guedes, Mary Fuhs, and Jorge Alberto dos Santos for help with the survey, and the IADB for letting us use test score data that they collected. Research reported in this publication was supported through the Research Support Budget and the Knowledge for Change Program (KCP) at World Bank, the NIA grant 1K01AG066999-01A1 (Carvalho), the ESRC for CEMMAP (ES/P008909/1) and the European Research
the policies of particular emphasis for our empirical analysis. In a model of imperfect competition in which domestic and foreign firms have capacity constraints, the foreign firm increases its exports to the domestic market at a “dumped” price when the foreign country’s own demand for the product falls. In this environment, it is welfare-improving for the importing country to impose import restrictions against the foreign country that is trying to export its way out of a recession. The cross-sectional empirical prediction of this model is that an importer will

> 6 Bagwell and Staiger (2003) build from an earlier model (Bagwell and Staiger, 1990) of self-enforcing trade agreements that links changes in trade policy to _iid_ trade volume shocks. Bown and Crowley (forthcoming) use a sample of annual US data from 1997-2006 to provide evidence from much more disaggregated, industry-level relationships in support of the Bagwell and Staiger (1990) theory.

6
**Table 3. Average landholding size (acre) and MPCE of farmers with different types of irrigation pump**

|**Farmer Type**|**Land Holding (Acre)**|**Monthly Per Capita Expenditure**<br>**(In Rs.)**|
|---|---|---|
|Don’t use a pump|1.9|738|
|Own a diesel pump|2.8|808|
|Rent in a diesel pump|2.1|690|
|Own an electric pump|3.3|889|

_Source: Created by authors using primary data collected from farmers_ Access to grid electricity in Jharkhand has improved rapidly over the last few years. The percentage of households with electricity connections increased from 32 percent in 2011 to 87 percent in 2019. The share of rural households using electricity as the primary source of light—as opposed to kerosene—increased from 20 percent in 2015 to 84 percent in 2019. Even as access to electricity increased, the duration and the quality of the power supply also improved. Between 2015 and 2018, about one-fifth of the rural households in Jharkhand moved from consuming electricity for 0-4 hours/day (tier 0) to 4-8 hours/day (tier 1).<sup>4</sup> Encouraged by the recent improvements in power supply, many farmers in Jharkhand are investing in electric irrigation pumps. Because the electricity consumption for irrigation is heavily subsidized in the state, cheap irrigation from the growing number of private electric pumps can make the community irrigation systems promoted by JSLPS and other institutions less attractive to farmers. These implications are discussed in more detail in the final section of the paper.

Paddy is the dominant crop in the Kharif season and the largest crop across all three seasons in terms of the cropped area. All farmers in our sample grow Kharif paddy. Pulses, millets, groundnut, and maize are other common
Empirically, our strategy consists of estimating a profit equation for each activity (“profit” meaning the net value of production approximated using the data available in the household survey) using a switching-regression technique. This gives us two expected profits for each farmer _i,_ conditional on _i_ ’s individual characteristics: one under the “subsistence” regime and one under the “market” regime. For a subsistence farmer, the difference in predicted profits between the market regime (where he is not) and the subsistence regime (where he is) is the opportunity cost of not being in the market, which of course depends on his individual characteristics. The present discounted value of this estimated stream of profit differentials for the marginal farmer is then our estimated entry cost.

As the decision to be in the market or not is endogenous to the profit differential, endogenous switching-regression methods must be used (Maddala and Nelson 1975). An additional difficulty comes from the fact that there is no natural definition of “subsistence farming”, as the proportion of farm output sold on the market is, in the data, a continuous variable.<sup>2</sup> Rather than imposing an arbitrary cutoff in terms of proportion of output self-consumed, we let the data determine the best way of splitting the sample using a two-step procedure developed for switching-regression models with unknown switch point. Namely, in the first step we obtain consistent estimates of all parameters for a given value of the unkown cutoff by maximum likelihood, taking endogeneity into account; in the second step, we search for the cutoff value that yields the _maximum maximorum_ of the log-likelihood function.

The threshold obtained by this method turns out to be at zero. That is, the sample is split between farmers who are completely cut from markets (about 10% of them) and farmers who sell something. This accords with the intuitive idea that indivisibilities are involved (purchase of capital equipment, availability of intermediaries, transport cost, etc.). Once those hurdles are
*<br>(0.043)<br>1002|0.062<br>(0.064)<br>991|1.27<br>[0.260]|
|E. I like the class of Science|0.153***<br>(0.055)<br>1007|0.151**<br>(0.070)<br>999|0.00<br>[0.970]|

_Source:_ Author's analysis based on experimental data generated for this study.

_Note:_ ** significant at 5 percent; *** significant at 1 percent. Robust standard errors in parentheses clustered at school level. The _p_ - values are in brackets in the third column. Controls: (a) students’ characteristics (gender, age, nationality, parent’s education, if the student missed, at most, one class per month, if the student has internet in his home, and if he/she has a car), (b) teacher characteristics (gender, age, years of experience, if she/he has postgraduate certificates), and (c) school characteristics (school size, seventh grade size, seventh repetition rate, FEPBA score in Language, school district, or location). The dependent variable in A-E represents a four-point scale, where 1 means strongly disagree, 2 disagree, 3 agree, and 4 strongly agree. The captivate index is a combined scale standardized in terms of the Control Group, whose construction is described in Appendix 4. All the regressions exclude the classrooms where the Human Body unit was not taught.

6
(1996) in the work on the 1988-89 survey uses scores of 'cognitive skills' available in the data set. The survey data contained the results of three tests of 'cognitive skills' applied to a sub-sample of individuals in the _9-55_ age category. These covered abstract reasoning (Raven's Colored Progressive Matrices), mathematics and reading (English) comprehension. The last two are used as measures of H, and the first as a measure of A in equation (1).

Turning to equation (2) above, in the absence of direct data which might measure school quality, Glewwe tries to control for variation in school quality by specifying interaction terms between years in school and the geographic region and age variables. "The former should control for variation in school quality across regions and the latter across age cohorts" (p.278).

In his estimation of equation (1) Glewwe finds that in the private sector reading skills have a significant impact on earnings, while in the public sector the mathematics score has a strong positive effect. Turning to the results of equation (2), "years of schooling alone is insignificant, but when interacted with age, Raven's score and 32
and total CERs to be generated by the end of the first commitment period by each major project type and methodology.

As can be seen from table 1, about 62 percent of the projects in the CDM pipeline are renewable resource based power generating projects accounting for 43.65 percent and 37.67 percent of the annual and total abatement during the first commitment period, respectively. Methane avoidance, coal bed/mine and cement is the second largest category in terms of number (17.04 percent) and annual abatement (18.32 percent), but HFCs, PFCs, and N2O reduction is the second largest category in terms of 2012 abatement (22.61 percent). Transport is the smallest and forestation is the second smallest category in terms of both number of projects and abatement. Large scale methodologies (AM) are applied to 6.87 percent of the projects, which account for 27.11 percent of the total annual abatement by the projects in the pipeline. Large scale consolidated (ACM) and small scale (AMS) methodologies are applied to 45.97 and 46.18 percent of the projects that account for 62.16 and 10.09 percent of annual abatement, respectively.

Following UNEP Risoe Center, the expected issuance of CERs for each individual project in each year over the life of the project is calculated by adjusting the annual average emissions abatement with an increment or decrement as reported in the database.<sup>10</sup> The crediting period is either 20 or 30 years for the afforestation and reforestation projects and either 7 or 10 years for all other types of projects. We consider the number of credit years as the duration of the project. In addition to CERs, some projects generate additional electricity output (in addition to the capacity of the baseline). The pipeline database reports the additional electricity generation capacity and expected hours of operation for individual projects. Using this data, the expected electricity output measured in megawatt hours (MWh) are calculated for each