rafmacalaba/datause-encoder-data huggingface dataset:
and a discussion with a trained enumerator. Comparing this method to rope and compass in southern Mali they find that on average plots areas were underestimated by 11 percent, with smaller plots being overestimated and larger plots underestimated, and measurement error being smaller for cotton fields than for cereals.

Farmer self-reported area estimates are influenced not only by plot and respondent characteristics, but also by a variety of cultural considerations and logistics of survey implementation. One major such factor is the prevalence of traditional or non-standard units. In many countries, respondents are often not familiar with standard measurement units such as acres, square meters, or hectares as they are used to express area measures in traditional units. Those units are not standardized and may vary in size by region, or even across villages or farms. In Ethiopia, for example, one of the most common non-standards units is the _timad_ , traditionally defined as the amount of land a pair of oxen can plough in one day. This measure will vary significantly by region, and even by farm. The soil texture and moisture content, plot slope and strength of the oxen will have an impact on the ease with which the oxen move and hence on the size of a timad. For Eastern Ghana, Goldstein and Udry (1999) report a correlation between selfreported and GPS measured plot size of just 0.15, which they attribute to the agricultural history of the region where local field measurements are traditionally based on length rather than area, and respondents are not accustomed to converting them to two dimensional area measures.

Recent studies emphasize not only the presence of large measurement error in self-reported measures, but also the systematic association of the magnitude and sign of the error with important plot characteristics. Carletto et al (2015), using LSMS-ISA data from Malawi (2010/11), Tanzania (2010/11), Niger (2011) and Uganda (2009/10), identify a common trend in the magnitude of measurement bias, defined as selfreported minus GPS area for plots for which both measures were available and taken independently. The smallest of plots (less than 0.5 acres) are systematically over-reported. The degree to which these are over-reported varies but in all countries the mean self-reported area is overestimated by at least 90% of the mean GPS area of plots in that particular class. With increasing plot size the degree of over-estimation decreases and, eventually, converts to under-estimation for the largest plots. Carletto et al (2013) find the same trend in 2005/6 data from Uganda while Dillon et al. provide evidence of similar systematic trends in data from Nigeria (2016). In a methodological study conducted in Southern Mali comparing area estimates with compass and rope measurement on larger plots (average 0.816 ha, maximum 8.78 ha) De Groote and Traoré (2005) find that the same is true: farmers (while aided by expert observers) are inclined to over-estimate the area of plots less than one hectare, while the degree of area under-estimation increases with plot size.

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Chirwa, E. W., & Dorward, A. (2013). _Agriculture Input Subsides; the recent Malawi experience_ (First Edit). Oxford: Oxford University Press.

- Dillon, A. (2010). _Do differences in the scale of irrigation projects generate different impacts on poverty and production?_ (IFPRI Discussion Paper No. 01022). _IFPRI Discussion Papers_ . https://doi.org/10.1111/j.1477-9552.2010.00276.x

- Ellis, F., & Maliro, D. (2013). Fertiliser Subsidies and Social Cash Transfers as Complementary or Competing Instruments for Reducing Vulnerability to Hunger: The Case of Malawi. _Developemnt Policy Review_ , _31_ (5), 575–596.

- Fan, S., Gulati, A., & Thorat, S. (2008). Investment, subsidies, and pro-poor growth in rural India. _Agricultural Economics_ , _39_ (2), 163–170. https://doi.org/10.1111/j.15740862.2008.00328.x

- FAO. (2011). _Guidelines for measuring household and individual dietary diversity_ . Rome, Italy.

- Fisher, M., & Kandiwa, V. (2014). Can agricultural input subsidies reduce the gender gap in modern maize adoption? Evidence from Malawi. _Food Policy_ , _45_ . https://doi.org/10.1016/j.foodpol.2014.01.007

- Garbero, A., & Songsermsawas, T. (2018). _Impact of modern irrigation on household production and welfare outcomes_ (IFAD RESEARCH SERIES No. 31). Rome, Italy.

- Gertler, P. J., Martinez, S., Premand, P., Rawlings, L. B., & Vermeersch, C. M. J. (2016). _Impact Evaluation in Practice_ (Second Edi). Washington, D.C.: World Bank. https://doi.org/10.1596/978-1-4648-0779-4

- Harou, A. P. (2018). Unraveling the effect of targeted input subsidies on dietary diversity in household consumption and child nutrition: The case of Malawi. _World Development_ , _106_ , 124–135. https://doi.org/10.1016/j.worlddev.2018.01.011

- Hoddinott, J., Berhane, G., Gilligan, D. O., Kumar, N., & Taffesse, A. S. (2012). The impact of Ethiopia’s productive safety net programme and related transfers on agricultural productivity. _Journal of African Economies_ , _21_ (5), 761–786. https://doi.org/10.1093/jae/ejs023

- Jumbe, C. B. L., & Nkhata, R. (2015). Does participation in communal water management improve household income? Evidence from Malawi. _Water Resources and Rural_

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increase in access to public secondary schools and a small reduction in access to private schools in subsequent years compared to baseline. This suggests that while in the past the private sector stepped in to provide schooling in underserviced peripheral areas, the significant increase in access to primary and secondary public schools that followed the mass influx of refugees gradually hollowed out private schooling in areas with greater exposure to refugee settlements.

The bottom row reports the effects for health access (proximity to health facilities, normalized by the population served, and weighted across five facility types), health utilization (averaged across utilization measures for children and their families sampled by the DHS), and road density (density within each parish-year weighted by road quality type). In 2006 (the baseline year for health outcomes), households and parishes with a higher level of refugee presence had worse health utilization and somewhat worse access. However, as with other service delivery domains, we find that compared to baseline, health access improved for more exposed parishes starting in 2011 (0.08 sd), aand continued to improve in 2016 (0.05 sd) and 2020 (0.05 sd). Health utilization also improved for more exposed parishes starting in 2011 (0.09 sd) and continued in 2016 (0.04 sd).

The bottom-right graph displays the results for roads: compared to the baseline year of 2011, when refugee presence was already positively associated with road density, the changes in the effect in 2016 (0.05 sd) and 2020 (0.11 sd) are positive and significant. SI Section S4.1 confirms that these results are robust to different specifications. Since we are using a DiD specification, SI Section S2 shows the lags and leads for the development outcomes.

# **5.2 Refugee Presence Does Not Lead to Backlash**

Our second set of outcomes proxy for a possible backlash. Figure 6 examines how refugees’ presence affects public opinion and insecurity. Recall that all Afrobarometer outcomes are standardized to have a mean of 0 and an SD of 1, whereas the ACLED outcome of _any violent event_ is binary (0 or 1). The top row of Figure 6 focuses on attitudes towards migrants and migration policy; _higher values_ indicate being more _pro-migrant_ . Plot 1 shows that, compared to 2011, Ugandan citizens with a higher level of exposure to refugees are no more or less welcoming of migrants than Ugandans living in parishes with lower levels of refugee presence after 2014.

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Hiring lesser qualified teachers and encouraging cheaper types of schools are obviously feasible in many of the countries where public sector teacher pay is relatively high. A direct consequence would be to allow coverage to expand; at the same time the policy does not necessarily compromise student learning. A recent evaluation suggests that students in Mali's "ecoles de base" in fact outperform their counterparts in regular public schools; and that students taught by the "volontaires" in Senegal outperform pupils taught by the more qualified teachers.<sup>37</sup> Thus, although gains in coverage and improvement in classroom conditions generally materialize as part of the process of economic development, countries achieve better or worse results than other countries at comparable levels of economic development according to the policies they adopt, particularly with respect to budget priorities, teacher salaries, and the relative emphasis on expansion of coverage and decreases in pupil-teacher ratios.

# **5. Conclusion**

In this study we have used aggregate data to explore the relation between country wealth and various aspects of educational development; examine the underlying sources of differences in education among rich and poor countries; and raise questions about promising directions for policy development in the sector, particularly in the context of lower income countries. As countries become richer, they generally spend more per pupil, expand coverage of the school-aged population, lower the pupil teacher ratio, achieve improvements in student learning, enhance the efficiency of student flow patterns and sector operations, and improve equity in access to schooling and the distribution of public spending on education. On some dimensions, such as coverage, access and per pupil spending, the improvement follows a relatively steep rise with per capita GNP, whereas on other indicators, such as student learning, the rise is less steep, and on yet other indicators, such as the share of enrollments in private schools, there is hardly any relationship to the per capita GNP.

An appealing explanation for the generally superior outcomes in richer countries is that they have more resources to support well-functioning and effective systems of education. Indeed richer countries have more resources for education per school-aged child. But bigger budget allocations account for a modest share of the advantage, since public spending on education relative to the GNP rises only modestly as incomes grow. Focussing on primary

> 37 Fomba, M. (1996) "L'enseignment primaire au Mali: mode de financement et acquisition des eleves," PhD dissertation, Uinversite de Bourgogne, Dijon, France.
# 4. Empirical strategy and data

The main difficulty in implementing this model empirically is that the job opportunities faced by women at the local level are not directly observable. In labor economic analyses for industrial countries, job opportunities are assessed through the wage the women could earn. But this wage is not directly observable either. Indeed, women who do not work do not earn any wage. And for those who do work, their own wage is influenced by their personal characteristics and not only by job opportunities. A strategy that has become standard to address these issues is to simultaneously estimate a labor force participation function and a wage function, and to use the estimated parameters to predict the potential wage of working-age women on the basis of their observable characteristics, including their educational attainment, their work experience and their number of young children (Heckman 1974 and 1993).

This strategy is arguably less reliable when applied to developing countries, due to different nature of their employment structures. A majority of workers in industrial countries are wage earners, but most workers in developing countries are farmers or self-employed (World Bank, 2012). The prediction of potential wage earnings in developing countries is thus based on a much smaller sample, relative to the size of the labor force. And this smaller sample is potentially biased by self-selection, as wage earners are unlikely to be a random subset of the people at work. An additional bias often comes from the fact that surveys such as the NSS do not capture comparable wage information for all wage earners.

Other studies introduce labor market indicators as determinants of labor force participation. Job opportunities have been shown to have two opposite effects on participation. These effects can be illustrated by considering an unanticipated decline in labor demand. As the prospect of finding a job becomes more elusive, there is a “discouraged worker” effect: people simply abandon their job search and withdraw from the labor force. At the same time, as breadwinners lose their jobs, more spouses – typically housewives – seek employment to compensate for the decline in household income; this leads to an “added worker” effect. The magnitude of the former effect has generally been assessed using time series data, which allows to estimate the elasticity of the aggregate participation rate to the aggregate unemployment rate (Blundell et al. 1988, Blundell et al. 1993). The second effect has been quantified by looking at spouses’ transitions in and out of the labor force as their husbands transition in and out of employment (Lundberg 1985 and Maloney 1987, among others).

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the share of beneficiaries originating from households in the bottom 2 quintiles (poor) versus top 3 quintiles (non-poor). The results of the benefits and beneficiary incidence analysis suggests that about 62 percent of total EGPP beneficiaries come from poor households (34 percent for the lowest and 28 percent for the second lowest quintile). Given that the average beneficiary incidence of the bottom 2 quintiles among social assistance programs in the Low Middle Income Countries to which status Bangladesh reached in 2014, is about 38 percent,<sup>25</sup> the level of inclusion errors of EGPP seems to be relatively low.

The targeting performance of EGPP is an outcome of self-targeting and committee-based rationing. One way to improve targeting performance in self-targeted schemes such as the workfare is, as Ravallion (1991) suggests, to increase costs of program participation so only the most needy would participate. Local committee-based selection of participants may improve targeting by incorporating further information on characteristics of desired participants or worsen it by subjectively selecting beneficiaries to extract rents.<sup>26</sup> The extent to which there are oversubscription, rationing, and unmet demand for EGPP is difficult to assess because no data have been collected for those who applied for EGPP but were not selected. If a PMT-based targeting method is additionally applied to the current pool of EGPP participants and only households in the bottom 40 percent of the distribution of poverty scores are allowed to participate, the data suggest that the beneficiary incidence of the poor would increase to 73 percent. Targeting performance can even further improve if costs of participation (e.g., stigma) increase due to clear signaling for poverty targeting with an addition of a PMT-based selection mechanism. Unconditional cash transfers would not be able to achieve the outcome of self-targeting without the work requirement and opportunities that workfare offers, unless the PMT-based targeting alone outperforms selftargeting.

The second issue related to beneficiaries not receiving their full entitlements is associated with payment delivery, and this aspect of efficiency does not change even if cash transfer instead of workfare is introduced. Discrepancy between entitled and received benefits has commonly existed in social assistance programs, especially in the case of food aid, which provides support for transition from food aid to cash payment. As discussed earlier, even for EGPP, which pays benefits

> 25 This is from the Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE) database. 26 A study based on Pakistan’s Benazir Income Support Program suggests that politician picked beneficiary selection significantly showed favoritism towards well connected, less deserving households. See Haseeb and Vyvorny (2017).

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# **Figure 2. Consumption of industrial commodities and income**

## **A. Oil consumption per capita vs. GDP per capita**

Tonnes of oil equivalent per capita 4 <!-- Start of picture text --> 3<br>2<br>Other EM7<br>1 China<br>G7<br>South Korea<br>0<br>0 10,000 20,000 30,000 40,000 50,000<br>GDP per capita<br><!-- End of picture text -->

## **C. Coal consumption per capita vs. GDP per capita**

Tonnes of oil equivalent per capita 2.0 <!-- Start of picture text -->
1.5<br>1.0<br>Other EM7<br>0.5 China<br>G7<br>South Korea<br>0.0<br>0 10,000 20,000 30,000 40,000 50,000<br>GDP per capita<br><!-- End of picture text -->

## **E. Copper consumption per capita vs. GDP per capita**

<!-- Start of picture text --> Kg per capita<br>25 Other EM7<br>China<br>20 G7<br>South Korea<br>15<br>10<br>5<br>0<br>0 10,000 20,000 30,000 40,000 50,000<br>GDP per capita<br><!-- End of picture text -->

## **B. Natural gas consumption per capita vs. GDP per capita**

<!-- Start of picture text --> Tonnes of oil equivalent per capita<br>2.0<br>1.5<br>1.0<br>Other EM7<br>0.5 China<br>G7<br>South Korea<br>0.0<br>0 10,000 20,000 30,000 40,000 50,000<br>GDP per capita<br><!-- End of picture text -->

**D. Aluminum consumption per capita vs. GDP per capita**

<!-- Start of picture text --> Kg per capita<br>35<br>30<br>25<br>20<br>15<br>10 Other EM7<br>China<br>5 G7<br>South Korea<br>0<br>0 10,000 20,000 30,000 40,000 50,000<br>GDP per capita<br><!-- End of picture text -->

## **F. Zinc consumption per capita vs. GDP per capita**

<!-- Start of picture text --> Kg per capita<br>14 Other EM7<br>China<br>12<br>G7<br>10 South Korea<br>8<br>6<br>4<br>2<br>0<br>0 10,000 20,000 30,000 40,000 50,000<br>GDP per capita<br><!-- End of picture text --> Sources: BP Statistical Review, World Bank, World Bureau of Metal Statistics.

A.-F. GDP per capita in constant 2010 U.S. dollars. Lines show the evolution of income and commodity consumption per capita over the period 1965-2016. Each data point represents one country or group for one year. Data for other EM7 are available from 1985-2016 for crude oil, natural gas, and coal, and 1992-2016 for aluminum, copper, and zinc. Other EM7 includes Brazil, India, Indonesia, Mexico, Russia, and Turkey.

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Bagwell and Staiger (2003) model dynamic, self-enforcing trade agreements that are characterized by trade policy that fluctuates in response to macroeconomic conditions.<sup>6</sup> They relate business cycles to tariff increases in a model with serially correlated shocks to growth. In this rich model, two large symmetric countries play a trade policy game in which each period’s one-shot game for every traded product is characterized by a terms-of-trade-driven prisoner’s dilemma. An international business cycle is modeled as fluctuations in the rate of growth of new product entry, and the international economy moves between periods of high growth and low growth according to two Markov-switching processes. Because of the possibility of terms-of-trade gains, the static welfare gain of a tariff hike increases with trade volume, and this would otherwise result in tariff increases being pro-cyclical. However, in the presence of the kind of positive, serially correlated growth shocks that give rise to recessions, cooperation to maintain low tariffs is more difficult in periods in which the expected rate of future trade growth is low. Thus, unilateral tariff increases are less costly in welfare terms during recessions with persistently underperforming growth because it is precisely then that the cost of a trade war is relatively low. This basic intuition generates the key empirical prediction of the model: import restrictions increase during recessions.

We turn to two partial equilibrium models (Crowley, 2010 and Knetter and Prusa, 2003) for guidance regarding two macroeconomic variables that exhibit considerable bilateral variation – real foreign GDP growth and real bilateral exchange rates. Crowley (2010) builds from the seminal, reciprocal dumping model of Brander and Krugman (1983) to show that import restrictions increase in response to macroeconomic weakness abroad. This theory focuses on the international rules regarding antidumping, one of 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.

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as sources of solution (though the authors do not provide measures of trust in technological fixes to verify such a link). Along this vein is the earlier mentioned work by Krosnic et al. 2006 which found that people stopped paying attention to global climate change when they realized that there is no easy solution for it, and many people only judged problems as serious if they thought they could be effectively acted upon.

Most work on concern, knowledge and perception has taken the form of large scale surveys. Data from interviews and ethnographic observation can yield information on meanings and relationships between thinking and feeling in everyday life. Results from the few studies that use interview data also do not support the information deficit model. Instead, their results describe a complexity of response, situations of knowing and not-knowing, and the emotional ambivalence that characterizes denial. For example Harriet Buckeley (2000) writes, “there is concern that public ignorance and illiteracy about global environmental issues is leading to misinformed views, apathy, ill-considered calls for government action, and little change in personal behavior. This view of the relations between public knowledge, values and actions accords with what has been described as an information deficit model: Ignorance about climate change is preventing appropriate public action” (ibid, 328). Instead, Buckeley notes that, “Confusion, doubt and a degree of illiteracy concerning climate science did not prevent focus group participants from locating this global issue in their backyard” (ibid, 322), and “there is a need to move from a narrow conception of public knowledge towards recognition of the complex, fluid and contradictory nature of public understanding of global environmental issues” (ibid, 329).

It is important to note that while there are significant criticisms to the information deficit model, the idea that people do not need any information regarding climate science to develop concern or engage in action is not one of them. In other words, many of the issues discussed by researchers here will be returned to under the section on cognitive barriers to action. What is important to draw from this section however, is that information alone is not enough to produce action, and that “information” like caring as will be discussed below, cannot be thought of as generic isolated blocks of “facts” with universal meaning and significance across all communities. Instead, information is socially structured, given social meanings, and must be understood in social context. As I will illustrate below, information on climate change may be 22