rafmacalaba/datause-encoder-data huggingface dataset:
incomes, among other things. For example they might be implied by Nash bargaining, by a “social choice process” within the household, or by competitive trade. Solution of this problem yields demands .

Alternatively, we can apply the second theorem of welfare economics to show that the equilibrium allocation can be supported by a set of individual income shares si such that Σsi = Y, with each individual solving yielding demand functions This approach can greatly simplify the comparative statics as well as empirical specifications. It also makes it very clear that if the model is estimated on household survey data with missing information on the xi and yij, then the income shares, si, cannot be computed and individual preferences parameters are not identified.

If the model is estimated on time use data, the individual utility functions can then include pure leisure as an assigned good. Distinguishing pure leisure from time used for producing yj, j = 1,..., n, and denoting it by ti, the individual solves the problem

|max ui(xi, yi1, ..., yin, ti) = ui|(20)|
|---|---|
|s.t.<br>xi+Σjpjyij+ witi= si|(21)|
|to yield demand functions||
|xi= xi(p1, ..., pn, wi, si)|(22a)|
|yij= yij(p1, ..., pn, wi, si)|(22b)|
|ti= ti(p1, ..., pn, wi, si)|(22c)|

households, the higher an individual's productivity the lower the implicit price of the good she produces 25
This study finds that the food price crisis in 2008–2010 in Pakistan had a negative impact on children’s schooling. Yet the effects are complicated, given household differences in welfare status, gender, access to agricultural lands, and the presence of older brothers and sisters. These findings can help further strengthen the strategies for improving children’s education, particularly for girls.

This paper is organized as follows: section 2 discusses the theoretical framework used to examine how food prices affect children’s education and labor; section 3 describes the data and empirical strategies; section 4 presents the regression results; the last section concludes and discusses the policy implications.

# **2. Theoretical Framework**

There is a rich amount of literature on the theory and empirical evidence regarding the allocation of children’s time and the association between household income and child labor. The “luxury axiom” proposed by Basu and Van (1998) in their seminal paper suggests that children work only when their families are unable to meet their basic needs. This axiom suggests a strong link between child labor and poverty and has been supported by many cross-sectional studies (Maitra and Ray 2002; Ersado 2005) and panel data studies (Edmonds 2005). However, other studies have cast doubt on this axiom by suggesting a more nonlinear relationship between poverty and child schooling and work decisions. For example, Bhalotra and Heady (2003) found a “wealth paradox”—children in land-rich households are more likely to work and less likely to attend school than children in land-poor households. Swaminathan (1998) showed that in Gujarat, India, economic growth led to increases in wage employment among children.

Building on the literature, a simplified theoretical framework (Appendix 1) was developed, which illustrates the potential impact of a food price increase on the allocation of children’s time. As expected, the theoretical framework shows that the impacts of a food price increase on children’s activities are contingent on household income, and the relationship is not necessarily linear. Thus, in the following empirical strategy, the heterogeneous effects of a price increase on children’s schooling and child labor by household economic status was tested and measured by 4
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when  <-0.15 in the case of the corruption experiences question, and when  <-0.04 for the corruption perceptions question. This represents less than one-quarter of the plausible range for  indicated on the horizontal axis in each figure.

We conclude from this that it is _a priori_ very plausible that there are in general causal effects running in both directions between corruption and confidence in public institutions. In this paper we are concerned primarily with the channel from corruption to confidence. While we are unable to formally isolate this channel using credible instruments given data limitations, we nevertheless argue that there are at least two reasons why the results we show are at least partially interpretable as a causal effect from corruption to confidence. The first is that, as we have discussed, it is much more difficult to see the channel for potential reverse causation in the results using the corruption experiences question. The second is that, even if reverse causation were present, it would need to be extremely strong in order to undermine our conclusion of a statistically significant effect of corruption on confidence.

# **6. Why Does the Adverse Effect of Corruption on Confidence in Institutions Matter?**

Thus far we have documented a strong negative relationship between corruption and confidence in public institutions. We conclude by investigating the consequences of this loss of confidence, finding that reduced confidence in public institutions leads to a reduction in political participation, raises support for violent means of political expression, and increases the desire of respondents to 'vote with their feet' and emigrate.

We draw on a number of questions from the GWP to measure these consequences of corruption-induced losses in confidence. To measure political participation, we use the GWP question which asks “ _In the past month, have you voiced your opinion to a public official?_ " As a measure of support for violent forms of protest, we use a question from the GWP which asks: _“Do you think groups that are oppressed and are suffering from injustice can improve their situation by peaceful means alone?”_ And finally, the desire to emigrate is captured by response to the question _"Ideally, if you had the_ 23
initializations have been employed that apportion the (residual) variation in the outcome to a single (presumed dominant) factor, effectively treating the second factor as orthogonal. Our insight is that these starting assumptions about the fixed effects are important because they become effectively locked-in from one iteration to the next. That is, the starting values represent a crucial identifying assumption for the variance decomposition. Furthermore, we show that the assumed form of the initial between-factor covariance can be explicitly controlled via introduction of a single initialization parameter. Denoted π ∈ �0, 1�, this parameter controls how the (residual) variation in the outcome is apportioned across the two factors to start the iteration procedure. In keeping with earlier discussion (Table 1), we hypothesize that corner values �π � 0, π � 1� will correspond to upper-bound factor models, such as described in rows 3 and 4 of Table 1, which rule out between-factor sorting. However, an agnostic or midpoint choice �π � 0.5� initially apportions the variation roughly equally across both factors. So, a corollary hypothesis is that this choice is likely to yield an upper bound on the magnitude of the between-factor covariance (sorting). As such, we do not propose a single correction for the potential bias in the estimates of the between-factor covariance. Instead, we provide a unified approach to the estimation of two-way fixed effects that makes it possible to impose – in a transparent way – alternative (starting) assumptions about the relationship between the two factors, including their covariance.<sup>12</sup> As we show, this serves to bound the estimates of interest.

# **3 Application to East Africa**

The previous section set out a general factor model for thinking about IOE and a unified empirical approach to decompose the variance within a two-way fixed-effects context. In the remainder of the paper we implement and compare results from three main choices for the fixed-effects initialization parameter, π, and validate our results by considering both alternative empirical methods and a wider range of choices for π. Based on our preferred results, we also go on to investigate heterogeneity in the variance contributions.

# **3.1 Data**

Our application of these methods uses test score data from East Africa. Since 2010, the Uwezo initiative has undertaken large-scale household surveys in Kenya, mainland Tanzania and Uganda (for further details and comparison to other regional assessments see Uwezo,

> 12 Note that extensions to more than two variables are possible in theory but add substantially to the number of covariance terms to be estimated, as well as the dimensionality of the choice space for the initialization process.

10
Goldin C. (1995). ―The U-shaped Female Labor Force Function in Economic Development and Economic History.‖ In Investment in Women’s Human Capital. Chicago: University of Chicago Press.

Hill M. and King E. (1995). ―Women’s education and economic well-being.‖ _Feminist Economics_ , Vol. 1 (2), pp. 21-46.

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22
This study makes several distinct contributions to the extant literature, specifically it: (i) explores how well water infrastructure can cope with extreme weather variability across a wide range of countries, most of which are in the developing world – where water infrastructure is likely to be less robust, (ii) establishes the relationship between water infrastructure quality and manufacturing firm performance using nationally representative firm-level data for 103 economies, (iii) quantifies the magnitude of this effect, and (iv) presents results that are differentiated by income level, according to the World Bank income classification.<sup>2</sup> The rest of the paper is structured as follows. Section 2 presents the data, section 3 outlines the empirical specifications used to examine, first, the impact of extreme weather events on water supply, and, second, to analyze the effect of supply shortages on firm performance, while section 4 presents the results of these analyses. Section 5 presents several robustness checks; finally, section 6 concludes.

# 2. Data

The firm-level data source is the World Bank Enterprise Surveys. For the analysis, over 16,000 manufacturing firms covering a cross-section of 103 countries between 2009 and 2015 are used.<sup>3</sup> The Enterprise Surveys (ES) provide a suitable data source for our analysis for several reasons. First, the surveys are nationally representative, stratified by location (within country), firm size, and sector. Second, the data are comparable across economies; the Enterprise Surveys (ES) use a standard survey instrument to collect data employing the same methodology across all countries; they target business owners and top managers of firms as respondents. Third, the surveys have high coverage in terms of countries and topics including access to finance, corruption, infrastructure, crime, competition, labor, business environment obstacles, and performance. The comparability of data across countries as well as the high coverage is a rarity given that many survey data sets face issues of low country coverage, no comparability across countries, or both. The Enterprise Surveys restrict the universe of firms given the nature of the questionnaire. The universe excludes extractive industries, agriculture, informal firms (not registered), fully government-owned enterprises and micro firms with fewer than five full time employees.

> 2 https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups

> 3 For a complete list of the countries included in the analysis, please refer to Table A2 of the Appendix.

5
most prevalent reasons were not being called back for the registration (13 percent), not enough information about the course (11 percent), studying (6 percent), too expensive/couldn’t afford it (6 percent), distance to the training center (4 percent), difficulty in finding accommodation (4 percent), and not interested in the training subjects (4 percent).

The lesson for future attempts to provide vocational training as an alternative to irregular migration is that it may not be enough to make such training free – potential migrants may need help covering transportation and subsistence costs, as well as the opportunity cost of their time while participating in such a program.

# **4. Data and estimation methods**

## **4.1 Baseline data and estimating short-term effects**

Our baseline survey, in April-May 2019, contained questions about the knowledge, beliefs, and intentions individuals had about backway migration and migration to Senegal. Our knowledge questions asked factual statements (e.g. how much does it typically cost to migrate from The Gambia to Italy through the backway; True or False: Some backway migrants are forced to work for others without being paid; True or False: Gambians do not need a passport to travel to Senegal), and were used to construct pre-specified knowledge measures defined as the number of correct responses (see Appendix B for the full list of questions).

Our baseline beliefs and intentions questions then attempted to ask for subjective probabilities (Delavande et al, 2010). We attempted to make it easier for individuals with no schooling to answer percent chance questions on a 0 to 100 scale by using a dynamic slider, as suggested by Maffioli and Mohanan (2018), and went through demonstration questions asking about the chances of rainfall and going to market to illustrate the use. Beliefs about the riskiness of migration were then assessed by asking questions such as “Out of every 100 Gambians who try the backway to Europe, use the slide (marked from 0 to 100) to indicate how many do you think make it at least as far as Libya or Morocco?”, “are attacked or robbed?”, etc.). We use this to form a pre-specified index of standardized z-scores about these beliefs about the different risks (appendix B). At baseline we likewise mention intentions by using the dynamic slider to assess their percent chance of migrating to Europe and to Senegal in the next year.

21
16 The group subject to these provisions is the largest in terms of CEE-5 imports into the EC, accounting for between one fourth and one third of their industrial inports (see Table 5). It includes products of most industrial sectors (organic and inorganic chemicals, some leather products, cork and wood products, glass, some steel products not covered by the ECSC, copper and copper products, electric machinery, optical goods, plastics, footwear, clothing accessories, furniture, motor vehicles, toys, etc.). Furthermore, the group is the most diversified in terms of coverage in separate ITAs. In contrast to other groups, there are substantial differences in the lists of items in the individual Agreements. Lists for Hungary and Poland are more extended in terms of the number of CN items.'<sup>9</sup> Table 5: The "Quota/Five-Year-Delayed" Free Trade Group: Experts to the EC and Pre-ITAs Market Access

||Exports<br>1991|Quoas<br>for 199|Share in Indu<br>2 Exports t the|strial Index 1991<br>EC 1988=100|NTB Coverage<br>Ratio,|Simple Ave<br>Tariff Rate,<br>percent|rage Range of<br> <br>tariff rates<br>Max-Min|
|---|---|---|---|---|---|---|---|
|Bulgaria|(in mill<br>111|ion of 11<br>na|8$)<br>16.3|- --- (in p<br>243|ercent)<br>18.8|8.6|25.84.0|
|FCSK|1230|379|26.5|213|20.6|8.7|25.8-0.0|
|HungarY|810|484|24.3|175|21.0|0.0|0.0-0.0|
|Poland|1477|575|23.6|213|21.7|0.0|0.0-0.0|
|Romania|516|366'|31.4|83|23.7|0.0|0.0-0.0|

'for 1993 Sources: See Table 2 and lTAs (Annex 3).

Pre-ITAs market access was better for this group tan for all industial products. The NTB coverage ratio was lower (by 24 percentage points) than for all industrial products (see Table 2 and 5). The average tariff rate was higher than that on all industial products for Bulgaria and the FCSK,1 but lower for other CEE-5 countries. For the reasons discussed in Section II, the "real" average tariff rate for the latter

**'9** The total group, including items- specified in all ITAs, covers 678 eight-digit CN items of which 677 are subjea to GSP rates.

21 Imports from these two countries enjoyed GSP status in 1991. Hence, the average rare reported in SMART would be near zero, as almost all items in this group had GSP teatmet.
## **Figure 12 Most of the Distributional Changes are Attributable to the Between Component**

<!-- Start of picture text --> 0.015<br>0.005<br>-0.005<br>Change in the overall Theil index<br>-0.015<br>Change in the between-group (agri / non-agri) component of the Theil index<br>-0.025<br>-0.035<br>Yemen, Republic of Haiti Vietnam Honduras Guatemala Peru Philippines Paraguay Thailand Ecuador Jordan Cambodia Brazil Colombia Bolivia Guyana Jamaica Dominican Republic El Salvador Costa Rica Kazakhstan Panama Azerbaijan Venezuela, Rep. Bol. China,P.R.: Mainland Kyrgyz Republic Georgia Russia Moldova Ukraine Albania Tajikistan Bulgaria Indonesia Chile Senegal Tanzania Uzbekistan Mongolia Mauritania Côte d'Ivoire Benin Guinea Pakistan Mali Romania Ghana Nicaragua Gambia, The Cameroon Mexico Burkina Faso Nepal Sri Lanka Estonia Armenia Nigeria Uganda Hungary Turkey Burundi South Africa Poland Bangladesh India Kenya Ethiopia Lithuania<br><!-- End of picture text -->

# **5 Conclusions and policy messages**

Trade distortions in agriculture and food represent the last major bastion of protection and have proven to be the main point of contention in recent multilateral trade negotiations. Using a newly developed dataset and methodological approach for evaluating the poverty and inequality effects of policy reforms—the GIDD—this paper has evaluated the potential impacts of the removal of agriculture trade distortions on the global income distribution.

There are three main messages emerging from our analysis. First, the liberalization of agriculture and food markets is unlikely to have large effects on global poverty. Our results show that the incidence of extreme poverty could rise by 0.2 percent, while moderate poverty is likely to fall by 0.3 percent.

The second message is that these small aggregate changes are produced by a combination of offsetting trends at the regional and country levels. With the elimination of all agriculture trade distortion, extreme poverty is reduced in all regions but in the Middle East and North Africa, where it is almost stable, and in South Asia, where it increases considerably. Since about 50 percent of all poor people live in South Asia, the worsening of poverty in this region counterbalances all the gains in the other parts of the world and an additional 9 million people fall into poverty. At the moderate poverty line, 14 million people escape poverty and most regions benefit from lower poverty incidence with the exclusion of Eastern Europe and Central Asia and Middle East and North Africa. Many non-agriculture households in South Asia are clustered below the 2 dollar a day poverty line and trade reform-related improvement in their incomes, versus the agricultural incomes’ decline, explain the difference in global poverty results when the 1 dollar or the 2 dollar a day lines are used.

23
<!-- Start of picture text --> Figure 5<br>Evolution of Official vs. Overall GDP, 1989-1994 averages<br>-90<br>-80<br>-70<br>-e GRG<br>-60<br>-50<br>O LTH<br>OD -40 -40 ~~~~~~~~~~~~~~LTV MDV<br>Cu U *~~~~~~~~~~~~~~ FSU<br>Q RMN BLS KZ* FSU<br>-30 0 UKR AZE o CEE<br>RUS * x FSU avg<br>BLG oE<br>Cu 0 ES1~~~~~~~~~~~~~~~~~~~~~~~~ CEavg<br>73 -20 - UZB CEE<br>-10 - PLD CZH<br>O L~~<br>0 -10 -20 -30 -40 -50 -60 -70 -80 -90<br>Cumulative % Change in Official GDP 1989-1994<br><!-- End of picture text -->