rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Consumption patterns are rapidly changing across the developing world, with prepared and packaged meals and meals consumed outside the home taking an ever growing share of the households’ food budget. Furthermore, with rising incomes, urbanization, and women entering the labor force, among various reasons, this trend is expected to persist as economies transition to middleincome status (USDA; Smith, 2013).

In spite of its growing participation in households’ budgets, food away from home (FAFH) remains one of the least studied aspects of food consumption. The vast majority of the literature corresponds to studies in the developed world, where the rising trend of eating out started a few decades ago. The main focus of that literature has been on public health issues, initially motivated by the high and positive correlation between eating out and obesity trends, and later also connected to other chronic diseases developed later in life (Burns et al., 2002; Guthrie et al., 2002; Kant and Graubard, 2004; Le Francois et al., 1996; Lin and Guthrie, 2012; Binkley et al., 2000). While causality has been hard to prove, sharp differences in the caloric and nutritional composition between food produced in commercial outlets and home-made food have been well documented, and interest in developing food-based dietary guidelines is increasing (Kearney et al., 2001; O’Dwyer et al. 2005).

The health consequences of eating out are also of interest in developing countries, but the role of food consumption in this setting has a much broader and deeper scope.<sup>2</sup> Food consumption plays an instrumental role in the design and monitor of development policy at the local, national, and global levels. Poverty, food security, health, and nutrition, lie at the heart of the development agenda, and the computation and monitoring of indicators that track those welfare dimensions rely heavily on food consumption or expenditure data. While data on household consumption or expenditure data has dramatically increased over the last few decades<sup>3</sup> , appropriate data on FAFH patterns are lacking, and the consequences of miss-measurement of food consumption on our assessment and understanding of these major policy areas are largely unknown.

> 2 With obesity increasingly becoming a pressing health issue in some middle-income countries, the link between eating out and obesity is also drawing attention in the developing world (Lozada et al., 2008; Bezerra and Sichieri, 2009).

> 3 The 1990 World Development Report on Poverty published by the World Bank relied on data from only 22 countries, and no country had more than one survey. Today, there are more than 850 surveys from 125 countries with consumption or expenditure data (Ravallion and Chen, 2011).

2
at least some aid in all months across all three years in our sample, the weather shock-related aid distribution occurred in September through the following March. We thus restrict our sample to households who were interviewed in September through March. The results for the four food consumption outcomes for the two-shock specification are provided in the bottom panel of Table 7.<sup>24</sup> Again, we see more positive interactions between aid interacted with the sequential shock, with higher calories per capita and a higher food consumption score for the IHPS sample, and higher value of food production and calories per capita for the FIAS sample.

> 24 Full results for the September-March sub-sample are in Online Appendix 9.

39
# **1 Introduction**

How do trade shocks impact workers? What can be done to facilitate labor adjustment in response to trade shocks? Answering these questions requires an understanding of how trade shocks impact not only wages but also labor mobility. If workers can easily move across occupations, sectors, or regions, they would readily pursue alternative job options following a trade shock. For this reason, there is now a substantial body of research studying the drivers and implications of imperfect labor mobility in the international trade context.<sup>1</sup> Empirically, labor mobility is pro-cyclical: it tends to increase with positive shocks and decrease with negative shocks. As an illustration of this empirical pattern, Figure 1 depicts a positive correlation between labor mobility and GDP growth using data from Brazil.

Figure 1: GDP growth rate and labor mobility, 2004-2015 <!-- Start of picture text --> 300<br>200<br>100<br>0<br>-100<br>-200<br>-300<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> _Notes_ : The figure shows the ratio between the number of movers (workers who switch occupation, sector, or region) and the number of stayers. We use the same definitions of occupation, sector, and region as those adopted in the main analysis of the paper. See Section 2 for data sources and the precise definitions of each variable. Both series are in terms of deviations from the mean and multiplied by the inverse of their standard deviations for comparison.

There is a simple and intuitive explanation for this empirical regularity: the number of job options available to workers tends to increase with positive shocks and decrease with negative shocks. If we conceptualize workers’ mobility decision based on the choice of a _job_ , rather than the choice of occupations, sectors, or regions–all of which have empirically invariant quantities– then their choice set should expand or shrink with shocks. Naturally, the size of the choice set impacts the probability of moving when there are switching costs, but existing discrete choice models cannot account for this relationship.<sup>2</sup> In this paper, we develop a novel general equilibrium discrete choice model that features endogenous expansion and contraction of the workers’ choice set. This new channel has important

> 1This literature includes contributions from Artu ̧c, Chaudhuri, and McLaren (2010), Dix-Carneiro (2014), Traiberman (2019), and Caliendo, Dvorkin, and Parro (2019), among others.

> 2In standard discrete choice models, shocks impact labor mobility as they can push the system out of steady state, but there is no reason for positive (negative) shocks to increase (reduce) labor mobility consistently. This paper, on the other hand, formulates a precise relationship between different dimensions of labor mobility and trade shocks.

1
# **3 Empirical Methodology**

## **3.1 Econometric Model**

We formally explore the relationship between uncertainty in import clearance times and firm export dynamics by estimating the following baseline model: The dependent variable _FirmDynamicsijkt_ is the entry, exit or survival rate of manufacturing firms in sector _k_ of country _i_ and exporting to destination _j_ . _Uncertaintyikt_ is the interquartile range of the time to clear imports at the border, and varies across time by exporting country _i_ and sector _k_ . We include vector _Xikt_ to control for time-varying exporting firm characteristics taken from the WBES and averaged at the country-sector-year level. It comprises (i) firm export intensity defined as direct exports as a fraction of total sales, (ii) import penetration, captured by the proportion of firms that import material inputs or supplies, (iii) the number of years since the firm first started to export as a proxy for average export experience, (iv) the natural logarithm of the number of permanent, full-time employees as a proxy for firm size, and (v) firm total factor productivity (TFP). Consistent with the literature on export dynamics, _Wij_ is a vector of standard time-invariant bilateral gravity variables such as contiguity, distance, and a binary variable for colonial relationship taken from CEPII. _EIAijt_ is a time-variant dummy provided at Jeffrey Bergstrand’s website and indicating whether the pair is involved in an economic integration agreement. _Tariffijkt_ refers to bilateral applied tariffs derived from WITS.<sup>22</sup> We also include vectors _Fit_ and _Pit_ to control for country-specific characteristics such as GDP per capita and the Doing Business indicator of the cost of procedures to start a business as a share of GNI per capita drawn from CEPII to account for the level of development and the average quality of the business environment, respectively. Finally, _Oi_ , _Dj_ , _Sk_ and _Tt_ refer to exporter-, importer-, sector- and year-specific effects respectively.

Table A3 in the Appendix provides a detailed description of the variables, while associated summary statistics are presented in Table 2. Given the preponderance of zeros for the dependent variables of interest, the baseline model is estimated by Poisson Pseudo-Maximum-Likelihood (PPML) following Santos Silva and Tenreyro (2006, 2010). Standard errors are clustered by exporter-importer pair.

> 22WITS uses the concept of effectively applied tariff which is defined as the lowest available tariff. If a preferential tariff exists, it is used as the effectively applied tariff. Otherwise, the Most-Favored Nation (MFN) applied tariff is used.

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Lavy, 2009), we experimentally vary motivation by offering half of the sample participants cash incentives for correct answers on a financial knowledge test (i.e., “pay for performance”). If individuals’ existing financial habits are deeply ingrained or “sticky,” they may not be interested in learning from financial training or may need external incentives to do so. Indeed, existing financial education studies have found low interest among adults in joining a financial education workshop, as well as relatively low attendance rates during the course of the program (Bruhn et al., 2013).

Second, we investigate the hypothesis that, although individuals may be motivated to learn, traditional financial education courses may be insufficient to foster the skills necessary to improve financial decisions because they do not address an individual’s unique needs. Specifically, we induce experimental variation in the intensity and personalization of financial education by delivering additional financial counseling services to half of the financial education treatment group. The effectiveness of such individual counseling has been well-proven in the medical and mental health fields, where interactions with counselors helps to adapt generic advice into actionable items that individuals can readily implement (e.g., Lerman et al., 1995; Marcus et al., 1998; Proper et al., 2003; Rimer and Kreuter, 2006). In our setting, this financial counseling treatment provides individuals with both intensive instruction and individualized advice, given that people in different situations need correspondingly different approaches. For example, participants can have a one-on-one tutorial with the counselor about the general steps in writing a budget. They may also ask for assistance in drafting a budget for their particular household, or seek advice on ways to minimize their own family’s expenses.

Finally, our third treatment explores behavior change barriers interrupting the link between financial knowledge and financial outcomes. That is, individuals may have the motivation to learn and may have adequate tools to make informed financial choices, but they may face behavioral constraints in converting their financial knowledge into concrete action (Mullainathan and Shafir, 2009 and 2013). We experimentally relax such behavioral impediments by encouraging half of the participants in the financial education treatment group to set short-term, visible, and achievable financial goals. By prompting respondents to designate target dates for completing financial planning goals (e.g., start putting money aside as saving each week), this intervention may provide 4
# **Appendix A: Trade Data and Tariff Rates for Tanzania’s Trade Partners**

# **Trade Data by Regional Partner and Sector**

To obtain the shares of imports and exports from the different regions of our model, we used trade data for 2007 obtained from WITS access to the COMTRADE database.

The regions of our model are Tanzania, the European Union, the East African Customs Union plus SADC and the Rest of the World. For the European Union, we took the 27 member countries as of 2007. In this appendix, we calculate and report data for the East African Customs Union and SADC separately. For the East African Customs Union, we took Kenya, Uganda, Rwanda and Burundi. For SADC we took Angola, Botswana, Democratic Republic of Congo, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, South Africa, Swaziland, Zambia and Zimbabwe. Trade shares for the “Africa” region in our model are the sum of East Africa Customs Union plus SADC. Rest of the World is the residual.

We mapped two digit sectors from the COMTRADE database into the sectors of our model. The exact mapping is defined in the first table below. We used Tanzania as the reporter country for both exports and imports. Results for both exports and imports are reported in the subsequent two tables.

# **Tariff Data.**

We were fortunate to receive unusually detailed collected tariff data from the Tanzania Revenue Authority. That is, we received data on collected import duties (tariffs) and import values at the eight digit tariff line level. The collected tariff rates for the sectors in our model are obtained by first aggregating the eight digit tariff line level tariff collections and import values to the sectors of our model. The ratio of tariff collections to import values for each sector of our model is then calculated to give estimates of the collected tariff rates, which in turn are incorporated into our SAM. The tariff rates are shown in Table 4 of the main text. Applying 98
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slowdown in potential growth is the continuation of a trend. This slowdown reflects sharp deteriorations in capital accumulation and productivity growth amid pronounced weakness in investment, as well as demographic changes. These constraints are unlikely to wane, but structural reforms can dampen their impacts or even counteract them.

# Figure 1. Growth forecast revisions since 2009

# A. Global growth revisions

<!-- Start of picture text --> B. Advanced economy growth revisions<br>Percentage points Initial forecast<br>3 Revised forecast<br>Difference<br>2<br>1<br>0<br>-1<br>D. EMDE regional growth revisions<br>Percentage points Average revision since 2016<br>Average revision since 2009<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>-1.0<br>-1.2<br>EAP ECA LAC MNA SAR SSA EMDEs<br>May-10 Dec-10 May-11 Dec-11 May-12 Dec-12 May-13 Dec-13 May-14 Dec-14 May-15 Dec-15 May-16 Dec-16 May-17 Dec-17 May-18 Dec-18 May-19<br><!-- End of picture text --> Percentage points Initial forecast 5 Revised forecast 4 Difference 3 2 1 0 -1 C. EMDE growth revisions Percentage points Initial forecast 8 Revised forecast Difference 6 4 2 0 -2

# C. EMDE growth revisions

Source: Consensus Economics, World Bank.

Note: EMDEs = Emerging market and developing economies, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa.

A-C. Output growth from the May and December consensus forecast publication of Consensus Economics since 2009. Revisions are the current forecast less the previous forecast for the current year. Weighted using constant 2010 U.S. dollar GDP for 2018.

D. Based on January and June forecasts of the World Bank’s Global Economic Prospects which achieves a better regional coverage than Consensus Economics.