rafmacalaba/data-use-mentions-extended huggingface dataset:
The key measure of SPID includes the (headcount) poverty rates estimated for each subnational unit using the daily poverty lines of $2.15, $3.65, and $6.85 and the relevant household income (consumption) surveys based on the GMD. That is, these poverty rates are calculated at the subnational level using household-level data and the specified poverty lines. The database also provides a multidimensional poverty index for a comprehensive perspective on poverty assessment. For assessing inequality, the SPID offers the Gini and Theil indexes (at the subnational unit level), which are the most common metrics for evaluating income inequality. All measurements are adjusted to real values using 2017 PPP dollars.

To examine the effects of warmer temperature on chronic poverty, we integrate the SPID data with the ERA5 satellite reanalysis data from the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ECMWF). The ERA5 offers hourly assessments of multiple climate-related variables across an approximate 0.25 longitude by 0.25 latitude degree resolution grid, with data accessible from 1979 onwards. In line with previous studies, we compile the gridded data at the regional level by determining area-weighted averages (i.e., averaging all grid cells within a given region) (Kalkuhl and Wenz, 2020).

# **4.2. Temperature Effects on Poverty**

Our review so far has considered the effects of temperature on both poverty and inequality due to their inherent connection. In the following empirical analysis, we narrow our focus to explore the link between temperature and poverty. This choice is driven by our interest in examining poverty dynamics, an area that has not been extensively covered in current studies. For a deeper look into the effects of temperature on inequality, readers can refer to the comprehensive study presented in Dang _et al._ (2023). We start with the analysis of temeprature effects on contemporary poverty in this section and present new estimates on poverty dynamics in the subsequent section.

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,” _Oxf. Econ. Pap._ , 60(2), 212–236.

- Hagerty, M. R. (2000): “Social comparisons of income in one’s community: evidence from national surveys of income and happiness,” _Journal of Personality and Social Psychology_ , 78, 764–771.

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- Hirschman, A., and M. Rothschild (1973): “The changing tolerance for income inequality in the course of economic development,” _Quarterly Journal of Economics_ , 87(4), 544–566.

- Hopkins, E. (2008): “Inequality, happiness and relative concerns: What actually is their relationship?,” _Journal of Economic Inequality_ , 6, 351–372.

- Inglehart, R. F. (1990): _Culture shift in advanced industrial society_ . Princeton University Press.

- Lepper, H. S. (1998): “Use of other-reports to validate subjective well-being measures,” _Social Indicators Research_ , 44(3), 367–379.

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|134984|122142|12842|87640|85988|29|416|11/96|
|Salta||||||||||||
|Before|3.090|.289|3.379|58941|52711|6230|40892|18000|16|260|3/96|
|After|2.381|.842|3.223|250860|240924|9936|201988|173074|15|301|11/96|
|Data Sources:|B.C.R.A.|and Fondo|Fiduciario|||||||||

Notes: The data on income and costs cover only 1993-96. Since Corrientes privatized in 1992, no pre-privatization data was available. For La Rioja, which privatized in 1994, pre-privatization data were not available because the bank had suspended operations in 1993. This leaves only the six cases listed above.

32 In some instances, owners of privatized banks are required to maintain a certain number of branches under the terms of their purchase agreements.

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, 2005). However, the personal diary was plagued with non-respondent problems in this experiment; about 54 percent of households assigned the personal diary did not complete it. Personal diaries also create the possibility of inadvertent double-counting of items consumed in common but reported separately by two or more individuals, and so care must be taken with this method. We discuss this further in the next section.

While the perfect diary experiment is, essentially, the benchmark for collecting consumption data through survey, it is difficult to extrapolate from experiments between diary and recall consumption modules if the implementation of the diary in the experiment does not reflect the reality of fielding a diary in developing or transition countries. It may be that in the case of illiterate (or simply unmotivated) respondents, a 7-day diary becomes a 7-day recall survey if the information has to be obtained orally by the interviewer at the end of the period. The implications of variation in literacy, motivation, and other factors, although not well-documented, implies that it can be quite difficult to conduct a high-quality diary survey, regardless of issues related to respondent recall bias.

In their study of measurement errors in food consumption, Ahmed et al. (2006) observe problems in the Canadian Food Expenditure Survey diary related to infrequency and diary exhaustion and conclude
Finally, we use several years of household surveys to investigate dynamic effects in the short and long terms and perform heterogeneous analysis to shed some light on the mechanisms underlying the main results.

Most studies that use dates of birth to estimate the long-term effects of a law or intervention, focus on the impact of early school enrollment. The literature outlines the educational channel as the main mechanism linking dates of birth to labor-market outcomes (see for example, Angrist and Krueger 1991; Bedard and Dhuey 2006; Black et al. 2011). A few empirical papers provide causal estimates for long-term effects of youth employment (or child labor) and outline the potential experience in the labor market as the plausible mechanism. This study therefore helps fill this gap in the empirical literature.<sup>3</sup> It is documented elsewhere (Piza and Souza, 2016) that the 1998 child-labor ban reduced labor-force participation rates of boys but not girls in the short term. This is so because the labor-market participation of girls was already low. They show that the decrease in participation rates of boys is driven by a fall in formal and informal work. Thus, Piza and Souza’s (2016) results suggest that the law was reasonably well enforced among males. In this paper, we will therefore concentrate only on the sample of males.<sup>4</sup> We find that, in the short term, the child-labor ban reduced the proportion of boys doing paid work and studying and increased the proportion of boys only studying. We also found that the ban reduced participation rates in the labor force and not just employment rates. We observe some persistence in these results until the affected cohort is age 18.

Our long-term analysis follows males between ages 22 and 29. Our estimates suggest that the ban did not have long-lasting effects on schooling attainment and labormarket outcomes of the affected cohort. We use distributional analysis in the long term to shed light on the potential impact of the ban on the subgroup of boys more
districts located 50-125 km from a highway, districts located 125-200 km from a highway, and districts over 200 km from a highway.

In all of our empirical work, our core focus is on the non-nodal districts of a highway. We measure effects for nodal districts, but the interpretation of these results will always be challenging as the highway projects are intended to improve the connectivity of the nodal districts. For the GQ network, we follow Datta (2011) in defining the nodal districts as Delhi, Mumbai, Chennai, and Kolkata. In addition, Datta (2011) describes several contiguous suburbs (Gurgaon, Faridabad, Ghaziabad, and NOIDA for Delhi; Thane for Mumbai) as being on the GQ network as “a matter of design rather than fortuitousness”. We include these suburbs in the nodal districts. For the NS-EW network, we define Delhi, Chandigarh, NOIDA, Gurgaon, Faridabad, Ghaziabad, Hyderabad, and Bangalore to be the nodal districts using similar criteria as that applied to the GQ network.

Tables 1a and 1b present simple descriptive statistics that portray some of the empirical results that follow. Table 1a starts by providing the count of districts by distance bands to the GQ network and by distance bands to the NS-EW network. As we do not need the panel nature of districts for these descriptive exercises, we retain some of the smaller districts that are not continuously measured to provide as complete a picture as possible (the total district count is 370). For both highway networks, roughly one-third of districts fall within 0-10 or 10-50 km from the network, with roughly two-thirds of districts over 50 km away from the network.

Panel A provides descriptive tabulations from the 1994 and 2000 data that come before the GQ upgrades, and Panel B provides similar tabulations for the 2005 and 2007 data that follow the GQ upgrades. Columns 2-4 provide aggregates of manufacturing activity within each spatial grouping, averaging the two surveys, and Columns 5-7 provide similar figures for young establishments less than four years old. Column 8 provides
Perhaps more likely would be a situation where absence of detailed information on the extent and distribution of poverty, and absent any specific effort to target the poor, would result in a default situation of resources being appropriated by the non-poor (see the discussion in Campante and Ferreira, 2003).  To the extent that this is true our estimates of the gains from targeting, once we assume some information on the distribution of poverty, might be seen as conservative estimates of the true benefits.

> 11 As we use predicted expenditures from census data unlike Ravallion and Chao (1988), who use observed income data from household surveys, we utilize a different algorithm to solve the optimization problem.  Applying their algorithm to our setting would yield the same results.

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# **1 Introduction**

It is well known that the difference in price levels across countries correlates with the difference in income levels. The main explanation for this observation, illustrated in Figure (1), is generally found in the Balassa-Samuelson effect (Balassa (1964), Samuelson (1964)) that links the differences in income levels with differences in productivity in the traded sector.

<!-- Start of picture text --> South Africa<br>0 .5 1 1.5<br>Relative Real Per Capita Income Relative to the US<br>2<br>1.5<br>1<br>.5<br>Relative Price Level (PPP/XR) to the US<br>0<br><!-- End of picture text --> Figure 1: Price Level and Income Level (source; Penn World Tables) In its original formulation, it states that a country will experience an appreciation of the equilibrium real exchange rate (i.e. an increase of home prices relative to foreign prices) when an increase in productivity in the _traded_ sector leads to an increase in local wages in _any_ (i.e. traded and non-traded) sector. The generalized increase in wages induces an increase in prices in non-traded sectors and thus an increase in the general level of prices.

The Balassa-Samuelson analysis then offers a guidance to understanding the process of sectoral changes during episodes of growth convergence in a small open economy, that is benefiting from productivity spillovers from global trade integration (Cravino and Haltenhof, 2017).

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# **2.4 Irrigation investments**

Government irrigation investments enabled the establishment of both small- and large-scale irrigation projects across the country. Initially, extension workers from the Ministry of Agriculture managed the schemes. However, with structural reforms for reduced government intervention in the 1980s, farmers were organized into cooperatives to take up all management responsibilities (Nkhata, Jumbe, & Mwabumba, 2014). Thus, government irrigation policy shifted from government-owned schemes to farmer-managed schemes (GoM 2001). Increasingly, more land is being used for irrigation farming. As of 2016, about 41,053 hectares of land benefiting 348,572 smallholder farmers was used for irrigation farming (Chafuwa, 2018). Despite the increases, the irrigated land is estimated at only 36 percent of the potential 407,862 hectares (draft National Irrigation Policy, 2022). Recent government irrigation investments with credit financing include, among others, the ongoing Shire Valley Transformation Project supported by the World Bank in Nsanje and the Linga irrigating scheme in Nkhatabay supported by the African Development Bank. During the dry season, individual farmers also privately perform irrigated farming, often in wetlands and along rivers. This practice, known locally as "dimba cultivation," is a supplement to rain-fed agriculture for farming households having access to such land. This study considers only access to and use of government irrigation investments.

# **3 Data and methodology**

# **3.1  Data sources**

The study uses Living Standards and Measurement Study (LSMS) data for Malawi; panel data for the years 2013, 2016 and 2019. The 2013 survey was conducted between April and October 2013; the 2016 survey field work happened between April 2016 and April 2017, and the 2019 survey was conducted between April 2019 and March 2020. The surveys included individual and community questionnaires collecting data on agriculture, employment, and consumption assets, among others, to explain the living conditions of Malawians. In Malawi the surveys are conducted by the National Statistical office with support from
._ Each cell represents a coefficient estimate using OLS regression relying on panel data estimation. The analysis is restricted to working age individuals in the years 2012 and 2018. In columns (1) to (4), the analysis is restricted to the sample of males. In columns (5) to (8), the analysis is restricted to the sample of females. The dependent variable is a dummy variable indicator equal to one if the individual is employed in the corresponding year 2012 and 2018 (according to the market definition of labor force participation). The main independent variable of interest is a dummy variable indicator equals to one if the individual reports that finding a job is one of the purposes of Internet use in the corresponding year 2012 and 2018. The regressions also include time-varying controls: age, a dummy variable for being married, three dummies for the individual’s educational attainment (primary or preparatory education, secondary education either general or vocational and above secondary education either post-secondary institute or university and above, and the reference category is no educational degree either illiterate individuals or literate individuals without any diploma), a dummy variable for rural areas as well as dummies for the various geographical regions included in the last column (Greater Cairo, Alexandria, and Suez cities, Urban Lower Egypt, Urban Upper Egypt, Rural Lower Egypt, and Rural Upper Egypt). The regressions also include individual and year fixed effects. The dependent variable mean is reported in the last row.

33
were recruited to assist with anthropometry and hemoglobin testing. The HemoCue Hb201+ photometer was used for hemoglobin testing. Locally-made height-boards and locally-sourced digital scales were used for anthropometry. During fieldwork, the nutritional assessment was conducted at a central location in each sampled village on the final day of interviewing. For complete details on the survey and fieldwork, see Buttenheim & McLaughlin (2006).

For analyses presented here, we restrict the sample in each year to children ages 3-14 living in villages with schools<sup>3</sup> . The analytic sample includes 10,748 children in 2006 and 9,810 children in 2008. Impact of school feeding is assessed along multiple dimensions: School participation is captured by a dummy variable for currently enrolled in school. Nutritional status is measured by height-for-age and weight-for-age standardized z-scores, calculated from measured height and weight and reported age using the WHO Child Growth Standards (WHO Multicentre Growth Reference Study Group, 2006); and by an indicator for whether the child is anemic, based on altitude-adjusted measured hemoglobin. (Nestel, 2002). The cutoff for anemia was 11.0 g/dL for children under 5 years, and 11.5 g/dL for children 5-10 years old.

# 5 Results

## **_5.1 Current enrollment_**

Current enrollment for children 6-14 by district, survey year and school feeding takeup is shown in Figure 1, with three important results. First, enrollment at baseline is higher in takeup villages than in non-takeup villages. Second, enrollment increases in all districts from 2006-2008,

> 3 Nineteen villages were excluded based on having no school. An additional village in Khua was excluded because no hemoglobin measures were taken there. The analytic sample includes 58 villages in Phongsaly, 63 villages in Khua, 59 villages in Nhot Ou, and 64 villages in Ngoi.

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problem-solving, decision-making and creation—and that there is more than one ability associated with intelligence. For example, Dweck’s “growth mindset” and Duckworth’s “grit” scales have been used to predict educational achievement and attainment as well as workforce outcomes (Duckworth et al., 2007; Dweck et al., 2011).

> 13 We specify years of schooling as a spline variable with knots at 9 and 13 years of education, although we also estimated our model using total years of schooling.

> 14 The survey data for the countries included in our study are restricted to urban households. We construct a variable to distinguish locations for each country that we considered to be a metropolis, or significantly different in terms of population density and economic activity.

> 15 Throughout the analysis we refer to _Υ_ as “returns” to skills inasmuch as the _β1, β2_ and _β3_ coefficients have been typically referred to as estimates of returns to schooling and experience. However, we acknowledge that the term “returns” is used in a loose sense because the costs of producing schooling and skills are ignored, as is a potentially causal relationship between skills and schooling.

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analyze cross-country variations in material deprivation in the 27 EU countries using latent trait analysis. Buligescu (2019) examines multidimensional poverty through the lens of various deprivation dimensions, focusing only on Romania and using data from the 2016 Household Budget Survey (HBS). Notably, the research diverges from traditional metrics like AROPE or those endorsed by the Oxford Poverty and Human Development Initiative (OPHDI). Instead, it bases its analysis on the European Union Chapter of the Human Rights Charter, offering a different perspective on multidimensional poverty, but it is not directly comparable to other methodologies.

**The last part of our study contributes to the literature on chronic poverty and material deprivations in Europe, and the literature on poverty and deprivations among the Roma population.** Whelan, Layte, and Maitre (2002) use the European Community Household Panel (ECHP) to explore the relationship between persistent income poverty and multiple deprivation in the EU. The study employs a weighted measure that accounts for the prevalence of each deprivation item across different countries and focuses on the intersection of persistent income poverty and deprivation, encompassing various aspects such as basic and secondary lifestyle deprivation, housing facilities, deterioration, and environmental issues. Biewen (2014) focuses on monetary poverty persistence and dynamics, primarily from an income-based perspective rather than considering multidimensional aspects. Papadopoulos (2016) examines the intersection between chronic material deprivation and long-term income poverty, using EUSILC data for the period 2005-2008, offering insights into persistent deprivation but without explicitly addressing multidimensional poverty.  Suppa (2023) uses EU-SILC panel data from 2016–2020 and the Alkire Foster approach to explore whether deprivations couple or decouple over time, estimating differences in deprivation transition probabilities between multidimensionally poor

> 13 <mark>Analyzing the chronic nature of AROPE is not possible, given that longitudinal EU-SILC microdata does not have the activity calendar; therefore, low work intensity cannot be estimated; we limit our analysis to the dimensions of AROPE that can be analyzed: chronic AROP and chronic material and social deprivation.</mark>
, consisting of their initial investment in plant and equipment (including the underground pipe network for the three villages of the commune), annual operating expenses, as well as the cost of a home connection and water meter. The water itself is pumped from a nearby river and hence is effectively free. Since the utility is privately owned and run as a business (albeit subject to government rate regulation), these figures probably represent the social opportunity cost of piped water provision in rural Vietnam quite accurately. Be that as it may, at the plant’s reported capacity of 10,000 customers and a plausible real interest rate of 5 percent, the implied _K_ works out to about 33 (in the appropriate units).<sup>33</sup>

> 32For concreteness, suppose that this planner commissions a study in which alternative ( _F, p_ ) contracts are randomly offered to potential customers and the resulting data are used to estimate take-up as a function of ( _F, p_ ). From the participation constraint, _e_<sup>_θ−αp_</sup> _≥ F_ , and a parametric assumption on _G_ , it is easy to see that both _G_ and _α_ are identified. Note that this experiment is different from the one actually conducted by Lee et al. (2020) in rural Kenya inasmuch as the latter only randomizes _F_ .

> 33Specifically, initial investment was reported to us as US$9.7 million in current dollars, annual operating costs at US$30,000, household connection cost at US$84. We convert all of these figures to monthly per customer flows. In the case of plant and equipment, we do so using a real interest rate equal to the lending rate minus the rate of inflation (both for 2019) as reported for Vietnam in World Bank statistics.

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