rafmacalaba/data-use-mentions-extended huggingface dataset:
second term is the utility-compensated cross-effect of the price of schooling on demand for children's leisure, or (equivalently, by symmetry of the Slutsky matrix) the effect of the price of leisure on schooling. The sign of this effect is ambiguous. It will be positive if schooling and leisure are (utility-compensated) substitutes. A sufficient condition for the program incentive to reduce child labor is that schooling and leisure are complements.

So it is unclear on theoretical grounds whether a reduction in the price of schooling generated by a higher stipend will reduce child labor; the extra time spent at school may well come out of children's leisure. And, by the same token, if the substitution effects between schooling and leisure are strong enough, child labor will not come at much cost to longer-term prospects of children escaping poverty. Our empirical work tests these effects.

# **4. Data and Estimation Methods**

We test whether work is a substitute for schooling using a household survey for Bangladesh, with a matched community survey. We use data from BBS's 1995-96 national Household Expenditure Survey (HES). We only use the rural sample in the HES, since the FFE program is not found in urban areas. The HES included questions on FFE participation. The survey did not include time use. We measure the incidence of child labor according to survey responses to the question: "What was your normal activity last week?". A child is deemed to be in the labor force if the answer was "employed", "employed but not working", "household work", or "seeking work". By this definition, 11.8% of boys and 12.1% of girls aged 5-16 in the sample were classified as being in the workforce. It is likely that this understates the extent of child labor, either because of deliberate under-reporting, or because relatively small amounts of part time work are not deemed to constitute the child's "normal activity".

10
# **3.2.2 The likelihood of success in a dispute**

Another element relating to the size of the expected benefits are factors affecting the probability of a successful economic resolution to a potential WTO dispute. Due to the self-enforcing nature of the WTO’s dispute settlement system, exporting countries can only enforce their rights through actual or implicit threats of retaliation against offending trading partners.<sup>19</sup> Therefore, we hypothesize that a U.S. remedy is more likely to be challenged if the affected foreign country is bilaterally powerful (with respect to the U.S.) because this positively affects the probability of a successful economic outcome. Conditional on a “guilty” Panel and/or Appellate Body ruling, the U.S. may be more likely to bring its WTO-inconsistent policy into conformity with its obligations if there is a credible retaliatory cost for failing to do so. We therefore measure the capacity for the foreign country to credibly threaten a tariff retaliation by using the share of U.S. total exports sent to the exporting country. To construct this measure, we use the U.S. bilateral export data provided in Feenstra et al. (2002).

Finally, in an additional specification that serves as a robustness check, we utilize additional explanatory variables designed to capture whether the affected country can contribute economic evidence of the U.S. failure to show injury to the domestic U.S. industry in the initial trade remedy investigation. The failure to find injury would be evidence to support the claim that U.S.-imposed remedy was WTO-inconsistent which, ceteris paribus, would result in a higher probability of winning the case by having a DSU panel find in the complainants’ favor.<sup>20</sup> We will discuss details of variables used for these measures in section 4.2 below.

# **3.3 Expected Costs to Initiating a Dispute Against the U.S.**

In this
Covariate shocks and deaths are not significantly associated with the likelihood of reporting depressive symptoms.

Next, we evaluate how large the effect of each type of event is on depression in monetary terms by comparing their effects alongside the coefficient of consumption expenditures (Table A1).<sup>16</sup> Using our regression specification, we can simulate how much extra consumption a respondent would need (the percentage point change in the depressive measure from consumption over the percentage change from each adverse event) in order to compensate for the effects of each incident on depression.

We find that the effects of idiosyncratic shocks and conflicts on depressive symptoms are equivalent to the loss of 3,852 and 7,914 naira, respectively. In other words, the experience of conflicts would need to be compensated with 7,914 naira (worth around US $52.27) to cancel out its impact on mental health.

# Additional correlates of depression

We also examined the correlation of some of individual characteristics with the likelihood of showing depressive symptoms. Our data set is novel in part due to the inclusion of consumption data along with that of mental health and adverse events, and as such, we can control for this. Lower lagged consumption per capita is, as expected, significantly correlated with lower levels of depressive symptomatology. This points to the persistence of well-being and its effect on mental health.

Another finding on significant covariates is notable: Married individuals are less likely to score over the CES-D cutoff than those who are single by about eight percentage points; the coefficient is similar for individuals who are married polygamously.

# Impacts of priming on reported depression

In addition to our analysis of the correlates of our depressive measure, we also look at the impacts of the survey experiment. The point estimate for the respondent priming indicator (i.e. respondents who answered questions about their adverse events before answering questions about their depressive symptoms) provides us with the causal impact of this priming. By itself, priming people by asking about their histories
While there are regulatory barriers that hinder the efficient allocation of resources across sectors, regions and firms, some critical markets also have inherent features that reduce contestability and facilitate anti-competitive behavior. This is the case for markets such as cement, fuel distribution and LPG. In 2014, Brazil’s Competition Authority CADE sanctioned 6 domestic cement companies that represented 75 percent of the domestic cement market for forming collusive agreements. While local cement sales had doubled in the past decade, prices had increased by around 33 percent. CADE estimates that customers overpaid around US$9 billion over the two decades the cartel existed.<sup>45</sup> A total of 14 cartel agreements have been uncovered by CADE in fuel retail and LPG markets between 2002 and 2015. In one of these, subsequent consumer savings were estimated at over U$ 10 million a year.<sup>46</sup>

# **_5. Potential productivity dividends accruing from stronger competition_**

Boosting competition in product markets is expected to bring positive payoffs for productivity growth in the manufacturing industry. The acceleration of trade integration combined with the removal of policy induced stringencies that hamper resource allocation would reduce distortions to market functioning while boosting competition in product markets. An econometric analysis is conducted to measure the association between productivity growth and competition intensity for Brazil. The analysis draws on the methodology presented at Aghion, Braun and Fedderke (2008) and uses data at CNAE 3-digit level from 2007–2014 Pesquisa Industrial Annual (see Annex 3 for a methodological presentation). The results suggest that higher price cost margins (implying that firms face lower levels of competition intensity) are significantly associated with lower (labor) productivity growth in the following year (see Figure 28).<sup>47</sup> The economic magnitude of the (average) “effect” is non-negligible: a 10 percent decrease from the manufacturing overall mean price cost margin of 0.14 is associated with an increase in productivity growth of 3.4 percent per year
Based on the LSMS data of the provinces of Hebei and Liaoning, our analysis suggests that the poor households gain more from non-farm activity than the rich households. One of the important reasons is that households that suffer stronger constraints in farm activity are more likely to participate in non-farm activity, and earn relatively higher income compared to those with better resources. Whether a household participates in non-farm activity depends on its incentive and capability. Households are motivated to undertake rural non-farm activity by either “pull” or “push” factors. If the non-farm sector has high returns, the “pull factors” will be strong; if farm activity cannot provide enough income for households (for example, if farm output is inadequate due to drought, flood, or insufficiency of land) or households need to diversify their income sources, the “push factors” may kick in. Poor households are less capable of weathering negative shocks, and are more risk averse. In order to have additional income as well as to diversify and undertake activities with returns that may have a low or negative correlation with those of farming, poor households may have stronger incentives to participate in non-farm activity; while rich households may have better capacity to do so thanks to their better endowments in physical and human capital (FAO, 1998). In rural China, the credit and insurance markets are underdeveloped. Households have strong incentives to diversify their income sources. However, because of their limited capacity and liquidity constraints, poor households tend to participate in non-farm activity with a high labor to capital ratio and low entry barriers.

The high participation in non-farm activity among low-income rural households may result in a more equal distribution of total income. For example, Adams and He (1995) and Adams (1999) argue that non-farm income reduces overall inequality in Pakistan and in Egypt, respectively. They suggest that households with low farm income (because of unequal access to land, etc.) are more likely to engage in non-farm activity, and the pro-poor distribution of nonfarm income
self-employment and 15 percent unpaid work (Choo and Majoka, 2020).<sup>4</sup> Informality has not evolved significantly over time: it was around 87.5 percent in 2008 compared to 88.5 percent in 2017.<sup>5</sup> Informality is pervasive across the income distribution: 70 percent of jobs in the top income quintile are informal (HIES 2016). Additionally, more than 40 percent of informal jobs in the top quintile correspond to non-agricultural self-employment. On the lower end of the income distribution, informality is associated with lower productivity or poor working conditions.

The Employees’ Old Age Benefits Institution (EOBI), at the federal level, provides age, disability and survivors pensions through a voluntary pension scheme to private sector employees on a self-registration basis. Only around 1.5 of 40 million workers were insured under the scheme in 2000 (Mahmood and Nasir, 2008). The Employees Social Security Institutions (ESSls) at the provincial level provide health services (through Social Security Hospitals, medical centers and dispensaries) and some cash benefits to employees of registered establishments. Punjab’s ESSI serves above 2.5 million patients every year and requires the employers to contribute 6 percent of the wages of their employees (having wages up to PKR 18,000).

While no non-contributory social pension exists in Pakistan, there are several well-established social protection programs to assist households in poverty. The main safety net program, run at the federal level, is an

> 4 The definition of informal employment used by the Pakistan Jobs Diagnostic is anyone without a formal employment contract. NOTE: The International Labor Organization (ILO, 2013) distinguishes between _the informal sector_ , which refers to the formality of production units and is usually linked to firms’ registration, and _informal jobs_ , which refer to workers’ type of employment.

> 5 These findings are based on the Labor Force Surveys of 2008 and 2017.

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providing evidence for professional upgrading. The absorption of Syrian refugees in the Turkish labor market seems facilitated by an increase in the number of new Syrian-owned businesses (Altindag, Bakis and Rozo 2018; Akgündüz, van den Berg, and Hassink 2018) with firms substituting capital with Syrian workers (Akgündüz and Torun 2018). Results on prices in Turkey remain unclear with different studies showing positive, negative or non-significant results. Only in Jordan there is some evidence for increases in rental prices in areas closer to refugee camps.

# Ethnic Greeks from Turkey to Greece

After the Greco-Turkish war of 1919–1922, 1.2 million Greek Orthodox were forcibly resettled from Turkey to Greece, increasing the Greek population by more than 20% within a few months. Murard and Sakalli (2018) look at the impact of this resettlement on local municipalities almost 100 years after the event. They find that localities with a greater share of refugees in 1923 have today higher earnings, higher levels of household wealth, greater educational attainment and larger financial and manufacturing sectors. These results are similar to the positive long-term effects due to agglomeration economies found for German expellees (Braun and Kvasnicka 2014; Schumann 2014; Wyrwich 2018) and for the forced population relocation in rural areas within Finland after World War II (Sarvimäki 2011).

# 2.3 Low-income countries

# Burundian and Rwandan refugees in Tanzania

Following the assassination of the Burundian president in 1993 and the Rwandan genocide in 1994, a large number of Burundian and Rwandan refugees settled in the Kagera region of neighboring Tanzania. By 1995, this region of 1.5 m inhabitants hosted about 0.7 m refugees from these two countries alone. This is the largest crisis in terms of incidence of refugees over the host population and also the crisis that has the largest number of empirical studies in Sub-Saharan Africa thanks to the considerable number of household surveys that have been conducted in the affected region over the years.

Alix-Garcia and Saah (2009) was one of the first studies that looked at the impact of
landowners failed to take advantage of this opportunity and instead entered long-term contracts on extremely unfavorable terms implies that, to allow all owners of land to benefit, additional efforts to disseminate land price information will be needed. Moreover, the fact that mortgage lending remained flat throughout the period suggests that the main expected benefit from liberalization of agricultural land markets remains to be realized. Reform to streamline registration and use of mortgages, reduce the transaction cost of selling land, and ensure market-based land valuation will be needed to harness such benefits and deepen domestic financial markets. Such transparent valuation can also avoid injustice of taxation due to the differential evolution of market prices for land across different regions or sectors.

Second, informality is not only a private issue but associated with negative externalities. Global experience suggests that, by generating rents for local intermediaries, informality may reinforce resistance to reform and weaken governance. Public action to get all cultivated land registered is thus warranted. Recovering associated cost through a recurrent land tax, the size of which is proportional to the increments in land value resulting from such a step, would be more viable and fairer than asking credit constrained individuals to shoulder associated cost upfront and also mechanisms while avoiding the risk of indirectly incentivizing land grabbing. Analysis also suggests that the statutory minimum lease term may encourage informality and make it more difficult for landowners to share in the benefits from reform.

Finally, while greater competition increases prices in rental markets, the opposite is the case in sales markets where dominant local players may be able to exercise some market power. Although the fact that land sales markets for legal entities opened only in 2024, together with the scant amount of registry data we were able to access makes it impossible to arrive at a confident conclusion, the importance of land sales markets as a determinant of Ukraine’s reconstruction and future development renders this an important issue for future research. Ideally such research would combine use of registry information with production data to link land market participation with productivity of land use and, in doing so
functional link with proximate data collection on national and local patterns of local labor mobility.

**Weather data.** Thanks to their panel nature, LSMS-ISA surveys can capture repeated weather shocks, cumulative weather risk, and their potential impact on migration outcomes. From a data collection perspective, to maximize this potential, it would be necessary to incorporate, within multi-topic surveys, a built-in and comprehensive set of (spatially and temporally) high-resolution weather data, ideally collected _in situ_ during the data collection efforts (for instance, through weather sensors), which should: i) not be limited to temperature and precipitation, but also incorporate other key weather variables such as humidity, windspeed, etc.; ii) include _ad hoc_ , agriculture-oriented weather information, such as weather indicators constructed on the basis on crop-specific and local growing season calendars; iii) also include, in addition to weather data, all the equivalent climate variables, based on the long-run averages (e.g., 30-year) of the weather time series. Should such weather data collection _in situ_ be unfeasible due to timing or budget constraints, efforts could be devoted to the integration of existing surveys with crowdsourced weather data (Minet et al., 2017; Muller et al., 2015; Overeem et al., 2013), to obtain the maximum possible spatial and temporal granularity of the climate information. The provision of a set of built-in weather data would prevent risks deriving from independent user integration with external remote sensing weather data, i.e., measurement error due to spatial anonymization and the sensitivity of econometric estimates to the specific weather data product chosen by the researcher (Micher et al., 2022), as well as the use of self-reported weather shocks based on the perceptions of the individuals interviewed – such as in Ocello et al. (2015) – whose accuracy remains questionable. The inclusion of longer-run climatic measures and time series would also allow the implementation of more recent and sophisticated techniques tailored to bridge the external validity gap between
<mark>The estimation of price-elasticities of demand from household budget surveys can face several empirical challenges. In the absence of price data, the use of unit values to approximate the prices faced by consumers can suffer from simultaneity bias (Deaton 1990; 2018).</mark><sup>13</sup> <mark>If a good is available at different quality grades in the market but prices paid are unobservable, the unit price inferred from household expenditure is a choice variable, reflecting both the consumer’s decision of price, and her decision over quality. In the case of tobacco taxation, this could raise concerns over “quality shredding”, where smokers facing a higher tax could adapt by smoking lower-priced or lower-quality tobacco, rather than reducing smoking. With quality shredding, the elasticity of cigarettes (based on survey data) could overstate smoker responsiveness. The ECBA results could hence overestimate the changes in tobacco expenditure and the sensitivity of indirect effects to a tax reform. This result of quality shredding, however, should be</mark> established empirically for each particular commodity and context (Deaton 2018).

<mark>The methodology proposed by Deaton (1990) to correct for simultaneity bias is applied as a robustness check to the estimation of price elasticities of demand for cigarettes in Georgia. While the Deaton method has been widely applied to estimate the demand for tobacco products, calculations by income groups are limited. Estimations of own-price elasticities of filtered cigarettes using the Deaton methodology at the tercile- and quintile-level are presented in</mark> <u><mark>Table 4 and Annex 6. The overall distributional pattern of</mark></u> <mark>elasticities is consistent with the results in</mark> <u><mark>Table 3. Lower-income groups of the population are</mark></u> <mark>systematically more responsive to price changes in cigarettes. On the other hand, the sensitivity of these Deaton results in
respondents forget details of events or forget events entirely, leading to reporting inaccuracies (Sudman and Bradburn, 1973). In agricultural surveys, recall length also often differs across the various activities recorded because farming activities, such as planting, plot maintenance, or harvest, take place at different times during the agricultural year. Depending on the timing of survey administration, respondents may be asked to recall the details of events sometimes many months in the past and the recall period may vary substantially across households or farms in the same sample if the fieldwork is completed over several months. In addition, some countries have two or more cropping seasons in one agricultural year and agricultural activities can be very different in both extent and nature depending on the season, which can make it even harder to recall events correctly.

Survey designers and planners have some control over the length of the recall period by choosing the number and timing of field visits to farms. This choice, however, has clear cost implications. Visiting households or farms multiple times in order to shorten the length of the recall period is more expensive and logistically demanding than collecting information only once during the agricultural year. In practice, surveys differ widely with respect to the number and timing of visits.<sup>3</sup> Survey designers may also choose to rely on more

> 1 Especially relevant are indicators 2.3.1 _Productivity of small-scale food producers_ and 2.3.2 _Income of small-scale food producers._

> 2 See, for example, Kasprzyk (2005), Zezza et al. (2016), Gaddis et al. (2019) and Kilic et al. (2018).

> 3 For example, the Tanzania National Panel Survey relies on one visit per household per year to collect information about Tanzania’s two cropping seasons. Data collection begins after the main season’s harvest and is rolled out over the course of 12 months. The AGRIS model, developed as part of the Global Strategy for Agricultural and Rural Statistics and